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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
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
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Finn, C., Abbeel, P., & Levine, S. (2017) · arXiv · doi:10.48550/arxiv.1703.03400 · Cited by 8,512
Recent advances in convolutional neural networks
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.013 · Cited by 6,576
2009 IEEE Conference on Computer Vision and Pattern Recognition
(2009) · doi:10.1109/cvpr14067.2009 · Cited by 5,550
Prototypical Networks for Few-shot Learning
Snell, J., Swersky, K., & Zemel, R. S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.05175 · Cited by 4,755
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
(2018) · doi:10.1109/cvpr40276.2018 · Cited by 4,737
2013 IEEE International Conference on Computer Vision
(2013) · doi:10.1109/iccv19984.2013 · Cited by 4,183
Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem
McCloskey, M., & Cohen, N. J. (1989) · Psychology of Learning and Motivation · doi:10.1016/s0079-7421(08)60536-8 · Cited by 3,770
Siamese Neural Networks for One-shot Image Recognition
Koch, G. E., Zemel, R. S., & Salakhutdinov, R. (2015) · Cited by 3,740
Generalizing from a Few Examples: A Survey on Few-shot Learning
Wang, Y., Yao, Q., Kwok, J. T., & Ni, L. M. (2021) · ACM Computing Surveys · doi:10.1145/3386252 · Cited by 3,165
One-shot learning of object categories
Fei-Fei, L., Fergus, R., & Perona, P. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.79 · Cited by 3,029
2009 International Joint Conference on Artificial Intelligence
(2009) · doi:10.1109/jcai15434.2009 · Cited by 2,800
Optimization as a Model for Few-Shot Learning
Ravi, S., & Larochelle, H. (2017) · International Conference on Learning Representations · Cited by 2,766
Distance Metric Learning with Application to Clustering with Side-Information
Xing, E. P., Jordan, M. I., Russell, S., & Ng, A. Y. (2002) · Cited by 2,700
The Effectiveness of Data Augmentation in Image Classification using Deep Learning
Perez, L., & Wang, J. (2017) · arXiv · doi:10.48550/arxiv.1712.04621 · Cited by 2,499
Deep Learning for Person Re-Identification: A Survey and Outlook
Ye, M., Shen, J., Lin, G., Xiang, T., Shao, L., & Hoi, S. C. H. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3054775 · Cited by 2,259
Signature Verification Using A "Siamese" Time Delay Neural Network
BROMLEY, J., BENTZ, J. W., BOTTOU, L., GUYON, I., LECUN, Y., MOORE, C., SÄCKINGER, E., & SHAH, R. (1993) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001493000339 · Cited by 2,077
Learning from Simulated and Unsupervised Images through Adversarial Training
Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., & Webb, R. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.241 · Cited by 1,814
2014 IEEE Conference on Computer Vision and Pattern Recognition
(2014) · doi:10.1109/cvpr20399.2014 · Cited by 1,774
Proceedings for the Annual Meeting of the Cognitive Science Society
Dziemianko, M., Clarke, A. D. F., & Keller, F. (2013) · Cited by 1,726
Meta-Transfer Learning for Few-Shot Learning
Sun, Q., Liu, Y., Chua, T.-S., & Schiele, B. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00049 · Cited by 1,264
Dynamic Few-Shot Visual Learning Without Forgetting
Gidaris, S., & Komodakis, N. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00459 · Cited by 1,128
Large-Scale Long-Tailed Recognition in an Open World
Liu, Z., Miao, Z., Zhan, X., Wang, J., Gong, B., & Yu, S. X. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00264 · Cited by 1,126
Zhang, C., Cai, Y., Lin, G., & Shen, C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01222 · Cited by 898
Meta-learning with differentiable closed-form solversPreprint
Bertinetto, L., Henriques, J. F., Torr, P. H. S., & Vedaldi, A. (2018) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.48550/arxiv.1805.08136 · Cited by 868
Matching Networks for One Shot Learning
Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., & Wierstra, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.04080 · Cited by 854
Few-Shot Learning via Embedding Adaptation With Set-to-Set Functions
Ye, H.-J., Hu, H., Zhan, D.-C., & Sha, F. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00883 · Cited by 790
Data Augmentation Generative Adversarial Networks
Antoniou, A., Storkey, A., & Edwards, H. S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.04340 · Cited by 747
Low-Shot Learning from Imaginary Data
Wang, Y.-X., Girshick, R., Hebert, M., & Hariharan, B. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00760 · Cited by 744
Meta-Learning for Semi-Supervised Few-Shot Classification
Ren, M., Triantafillou, E., Ravi, S., Snell, J., Swersky, K., Tenenbaum, J. B., Larochelle, H., & Zemel, R. S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.00676 · Cited by 694
One shot learning of simple visual concepts
Lake, B. M., Salakhutdinov, R., Gross, J. N., & Tenenbaum, J. B. (2011) · eScholarship (California Digital Library) · Cited by 687
Revisiting Local Descriptor Based Image-To-Class Measure for Few-Shot Learning
Li, W., Wang, L., Xu, J., Huo, J., Gao, Y., & Luo, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00743 · Cited by 654
Deep Convolutional Inverse Graphics Network
Kulkarni, T. D., Whitney, W., Kohli, P., & Tenenbaum, J. B. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1503.03167 · Cited by 653
Edge-Labeling Graph Neural Network for Few-Shot Learning
Kim, J., Kim, T., Kim, S., & Yoo, C. D. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00010 · Cited by 529
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few ExamplesPreprint
Triantafillou, E., Zhu, T., Dumoulin, V., Lamblin, P., Evci, U., Xu, K., Goroshin, R., Gelada, C., Swersky, K., Manzagol, P.-A., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.03096 · Cited by 523
Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning
Liu, Y., Lee, J., Park, M., Kim, S., Yang, E., Hwang, S. J., & Yang, Y. P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.10002 · Cited by 468
Adaptive Subspaces for Few-Shot Learning
Simon, C., Koniusz, P., Nock, R., & Harandi, M. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00419 · Cited by 450
Boosting Few-Shot Visual Learning With Self-Supervision
Gidaris, S., Bursuc, A., Komodakis, N., Perez, P. P., & Cord, M. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00815 · Cited by 409
Finding Task-Relevant Features for Few-Shot Learning by Category Traversal
Li, H., Eigen, D., Dodge, S., Zeiler, M., & Wang, X. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00009 · Cited by 400
Learning to Compare: Relation Network for Few-Shot Learning
Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P. H. S., & Hospedales, T. M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00131 · Cited by 380
Cross-Domain Few-Shot Classification via Learned Feature-Wise TransformationPreprint
Tseng, H.-Y., Lee, H.-Y., Huang, J., & Yang, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.08735 · Cited by 377
Few-Shot Learning approach for plant disease classification using images taken in the field
Argüeso, D., Picon, A., Irusta, U., Medela, A., San-Emeterio, M. G., Bereciartua, A., & Alvarez-Gila, A. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105542 · Cited by 369
Few-Shot Learning with Graph Neural Networks
García, V., & Bruna, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.04043 · Cited by 364
Data Programming: Creating Large Training Sets, Quickly
Ratner, A., De, C., Wu, S., Selsam, D., & Ré, C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.07723 · Cited by 350
Negative Margin Matters: Understanding Margin in Few-Shot Classification
Liu, B., Cao, Y., Lin, Y., Li, Q., Zhang, Z., Long, M., & Hu, H. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58548-8_26 · Cited by 350
A Broader Study of Cross-Domain Few-Shot Learning
Guo, Y., Codella, N. C., Karlinsky, L., Codella, J. V., Smith, J. R., Saenko, K., Rosing, T., & Feris, R. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58583-9_8 · Cited by 320
Deep Cross-Domain Few-Shot Learning for Hyperspectral Image Classification
Li, Z., Liu, M., Chen, Y., Xu, Y., Li, W., & Du, Q. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3057066 · Cited by 302
Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification
Xie, J., Long, F., Lv, J., Wang, Q., & Li, P. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00781 · Cited by 302
Multi-Scale Metric Learning for Few-Shot Learning
Jiang, W., Huang, K., Geng, J., & Deng, X. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.2995754 · Cited by 298
CrossTransformers: spatially-aware few-shot transferPreprint
Doersch, C., Gupta, A., & Zisserman, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.11498 · Cited by 274
Çakir, F., He, K., Xia, X., Kulis, B., & Sclaroff, S. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00196 · Cited by 272
Learning to Compose Domain-Specific Transformations for Data Augmentation
Ratner, A., Ehrenberg, H. R., Hussain, Z., Dunnmon, J., & Ré, C. (2017) · PubMed · doi:10.48550/arxiv.1709.01643 · Cited by 272
DPGN: Distribution Propagation Graph Network for Few-Shot Learning
Yang, L. Q., Li, L., Zhang, Z., Zhou, X., Zhou, E., & Liu, Y. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01340 · Cited by 270
Distribution Consistency Based Covariance Metric Networks for Few-Shot Learning
Li, W., Xu, J., Huo, J., Wang, L., Gao, Y., & Luo, J. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33018642 · Cited by 255
Improved Few-Shot Visual Classification
Bateni, P., Goyal, R., Masrani, V., Wood, F., & Sigal, L. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01450 · Cited by 239
Few-Shot Learning via Saliency-Guided Hallucination of Samples
Zhang, H., Zhang, J., & Koniusz, P. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00288 · Cited by 237
A Unified Approach for Conventional Zero-Shot, Generalized Zero-Shot, and Few-Shot Learning
Rahman, S., Khan, S., & Porikli, F. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2861573 · Cited by 228
Ring Loss: Convex Feature Normalization for Face Recognition
Zheng, Y., Pal, D. K., & Savvides, M. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00534 · Cited by 223
What Should Not Be Contrastive in Contrastive LearningPreprint
Xiao, T., Wang, X.-L., Efros, A. A., & Darrell, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.05659 · Cited by 223
BSNet: Bi-Similarity Network for Few-shot Fine-grained Image Classification
Li, X., Wu, J., Sun, Z., Ma, Z., Cao, J., & Xue, J.-H. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3043128 · Cited by 214
Self-citationTransfer Learning in a Transductive Setting
Rohrbach, M., Ebert, S., & Schiele, B. (2013) · MPG.PuRe (Max Planck Society) · Cited by 212
TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot LearningPreprint
Yoon, S. W., Seo, J. S., & Moon, J. J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.06549 · Cited by 210
Few-Shot Learning With Embedded Class Models and Shot-Free Meta Training
Ravichandran, A., Bhotika, R., & Soatto, S. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00042 · Cited by 199
Learning a Few-shot Embedding Model with Contrastive Learning
Liu, C., Fu, Y., Xu, C., Yang, S., Li, J., Wang, C., & Zhang, L. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i10.17047 · Cited by 198
Knowledge-Guided Multi-Label Few-Shot Learning for General Image Recognition
Chen, T., Lin, L., Chen, R., Hui, X., & Wu, H. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3025814 · Cited by 197
Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning
Qiao, L., Shi, Y., Li, J., Tian, Y., Huang, T., & Wang, Y. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00370 · Cited by 196
Low-Rank Pairwise Alignment Bilinear Network For Few-Shot Fine-Grained Image Classification
Huang, H., Zhang, J., Zhang, J., Xu, J., & Wu, Q. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3001510 · Cited by 195
TADAM: Task dependent adaptive metric for improved few-shot learning
Oreshkin, B. N., Rodríguez, P., & Lacoste, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.10123 · Cited by 191
Infinite Mixture Prototypes for Few-Shot LearningPreprint
Allen, K. R., Shelhamer, E., Shin, H., & Tenenbaum, J. B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.04552 · Cited by 190
Momentum contrastive learning for few-shot COVID-19 diagnosis from chest CT images
Chen, X., Yao, L., Zhou, T., Dong, J., & Zhang, Y. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.107826 · Cited by 185
Hierarchical Graph Neural Networks for Few-Shot Learning
Chen, C., Li, K., Wei, W., Zhou, J. T., & Zeng, Z. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3058098 · Cited by 183
A Closer Look at Few-shot Classification
Chen, W.-Y., Liu, Y., Kira, Z., Wang, Y.-C. F., & Huang, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.04232 · Cited by 174
Collect and Select: Semantic Alignment Metric Learning for Few-Shot Learning
Hao, F., He, F., Cheng, J., Wang, L., Cao, J., & Tao, D. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00855 · Cited by 167
Towards Open-World Person Re-Identification by One-Shot Group-Based Verification
Zheng, W., Gong, S., & Xiang, T. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2453984 · Cited by 163
Zhang, J., Zhao, C., Ni, B., Xu, M., & Yang, X. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00177 · Cited by 160
Self-citationPiecewise Classifier Mappings: Learning Fine-Grained Learners for Novel Categories With Few Examples
Wei, X.-S., Wang, P., Liu, L., Shen, C., & Wu, J. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2924811 · Cited by 159
A Two-Stage Approach to Few-Shot Learning for Image Recognition
Das, D., & Lee, C. S. G. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2959254 · Cited by 152
A concise review of recent few-shot meta-learning methods
Li, X., Sun, Z., Xue, J., & Ma, Z. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.05.114 · Cited by 145
Self-citationCross-domain Few-shot Learning with Task-specific Adapters
Li, W., Liu, X., & Bilen, H. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00702 · Cited by 140
PARN: Position-Aware Relation Networks for Few-Shot Learning
Wu, Z., Li, Y., Guo, L., & Jia, K. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00676 · Cited by 136
Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive ProcessesPreprint
Requeima, J., Gordon, J. A. R., Bronskill, J. F., Nowozin, S., & Turner, R. E. (2019) · arXiv (Cornell University) · doi:10.17863/cam.47574 · Cited by 136
S3Net: Spectral–Spatial Siamese Network for Few-Shot Hyperspectral Image Classification
Xue, Z., Zhou, Y., & Du, P. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3181501 · Cited by 124
Few-Shot Learning With Global Class Representations
Li, A., Luo, T., Xiang, T., Huang, W., & Wang, L. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00981 · Cited by 120
Local descriptor-based multi-prototype network for few-shot Learning
Huang, H., Wu, Z., Li, W., Huo, J., & Gao, Y. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.107935 · Cited by 114
Li, X., Shi, D., Diao, X., & Xu, H. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3109268 · Cited by 111
Self-citationUniversal Representation Learning from Multiple Domains for Few-shot Classification
Li, W., Liu, X., & Bilen, H. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00939 · Cited by 107
Few-Shot Classification with Contrastive Learning
Yang, Z., Wang, J., & Zhu, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20044-1_17 · Cited by 107
Machine Learning - Algorithms, Models and Applications
Sen, J. (2021) · Artificial intelligence · doi:10.5772/intechopen.94615 · Cited by 101
Meta-DermDiagnosis: Few-Shot Skin Disease Identification using Meta-Learning
Mahajan, K., Sharma, M., & Vig, L. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00373 · Cited by 99
LGM-Net: Learning to Generate Matching Networks for Few-Shot LearningPreprint
Li, H., Dong, W., Mei, X., Ma, C., Huang, F., & Hu, B.-G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.06331 · Cited by 90
Huang, H., Zhang, J., Yu, L., Zhang, J., Wu, Q., & Xu, C. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3065693 · Cited by 89
Learning Task-aware Local Representations for Few-shot Learning
Dong, C., Li, W., Huo, J., Gu, Z., & Gao, Y. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/100 · Cited by 88
Small Sample Learning in Big Data EraPreprint
Shu, J., Xu, Z., & Meng, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.04572 · Cited by 86
Attentional Constellation Nets for Few-Shot Learning.
Xu, W., Xu, Y., Wang, H., & Tu, Z. (2020) · International Conference on Learning Representations · Cited by 85
MELR: Meta-Learning via Modeling Episode-Level Relationships for Few-Shot Learning
Fei, N., Lu, Z., Xiang, T., & Huang, S. (2021) · International Conference on Learning Representations · Cited by 81
Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning
Zhang, H., Koniusz, P., Jian, S., Li, H., & Torr, P. H. S. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00931 · Cited by 81
Zero and Few Shot Learning With Semantic Feature Synthesis and Competitive Learning
Guan, J., Lu, Z., Xiang, T., Li, A., Zhao, A., & Wen, J.-R. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2965534 · Cited by 80
Task-aware Part Mining Network for Few-Shot Learning
Wu, J., Zhang, T., Zhang, Y., & Wu, F. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00832 · Cited by 79
Transductive Relation-Propagation Network for Few-shot Learning
Ma, Y., Bai, S., An, S., Liu, W., Liu, A., Zhen, X., & Liu, X. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/112 · Cited by 73
Attribute-Guided Feature Learning for Few-Shot Image Recognition
Zhu, Y., Min, W., & Jiang, S. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.2993952 · Cited by 71
Spatial Contrastive Learning for Few-Shot Classification
Ouali, Y., Hudelot, C., & Tami, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86486-6_41 · Cited by 70
Robust Few-Shot Learning for User-Provided Data
Lu, J., Jin, S., Liang, J., & Zhang, C. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2984710 · Cited by 69
Knowledge Graph Transfer Network for Few-Shot Recognition
Chen, R., Chen, T., Hui, X., Wu, H., Li, G., & Lin, L. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6630 · Cited by 62
Concept Learners for Few-Shot LearningPreprint
Cao, K., Brbić, M., & Leskovec, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.07375 · Cited by 61
Rectifying the Shortcut Learning of Background for Few-Shot LearningPreprint
Luo, X., Wei, L., Wen, L., Yang, J., Xie, L., Xu, Z., & Tian, Q. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.07746 · Cited by 60
Attentive Prototype Few-Shot Learning with Capsule Network-Based Embedding
Wu, F., Smith, J. S., Lu, W., Pang, C., & Zhang, B. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58604-1_15 · Cited by 50
Variational Metric Scaling for Metric-Based Meta-Learning
Chen, J., Zhan, L.-M., Wu, X.-M., & Chung, F.-L. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5752 · Cited by 45
SEN: A Novel Feature Normalization Dissimilarity Measure for Prototypical Few-Shot Learning Networks
Nguyen, V. N., Løkse, S., Wickstrøm, K., Kampffmeyer, M., Roverso, D., & Jenssen, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58592-1_8 · Cited by 42
Temperature network for few-shot learning with distribution-aware large-margin metric
Zhu, W., Li, W., Liao, H., & Luo, J. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2020.107797 · Cited by 41
Low-Shot Face Recognition with Hybrid Classifiers
Wu, Y., Liu, H., & Fu, Y. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.228 · Cited by 36
Auto-FSL: Searching the Attribute Consistent Network for Few-Shot Learning
Zhang, L., Wang, S., Chang, X., Liu, J., Ge, Z., & Zheng, Q. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3076523 · Cited by 35
Person re-identification based on metric learning: a survey
Zou, G., Fu, G., Peng, X., Liu, Y., Gao, M., & Liu, Z. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10953-6 · Cited by 33
Semi-Siamese Training for Shallow Face Learning
Du, H., Shi, H., Liu, Y., Wang, J., Lei, Z., Zeng, D., & Mei, T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58548-8_3 · Cited by 30
XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot LearningPreprint
Yoon, S. W., Kim, D.-Y., Seo, J. S., & Moon, J. J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.08561 · Cited by 29
Re-ranking for image retrieval and transductive few-shot classification
Shen, X., Xiao, Y., Hu, S. X., Sbai, O., & Aubry, M. (2021) · HAL · Cited by 26
Zhou, Y., Guo, Y., Hao, S., & Hong, R. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3170727 · Cited by 25
AIFS-DATASET for Few-Shot Aerial Image Scene Classification
Li, L., Yao, X., Cheng, G., & Han, J. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3149507 · Cited by 24
Bridging Few-Shot Learning and Adaptation: New Challenges of Support-Query Shift
Bennequin, E., Bouvier, V., Tami, M., Toubhans, A., & Hudelot, C. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86486-6_34 · Cited by 14
On sensitivity of meta-learning to support dataPreprint
Agarwal, M., Yurochkin, M., & Sun, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.13953 · Cited by 8
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
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
