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

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Meta-learning as a promising approach for few-shot cross-domain fault diagnosis: Algorithms, applications, and prospects

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Three-dimensional singular spectrum analysis for precise land cover classification from UAV-borne hyperspectral benchmark datasets

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Transformer-based meta learning method for bearing fault identification under multiple small sample conditions

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Few-shot remote sensing image scene classification: Recent advances, new baselines, and future trends

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Few-Shot Class-Incremental Learning for Classification and Object Detection: A Survey

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Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image Classification

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Few-shot learning based on deep learning: A survey

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Global- and local-aware feature augmentation with semantic orthogonality for few-shot image classification

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HyRSM++: Hybrid relation guided temporal set matching for few-shot action recognition

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Self-reconstruction network for fine-grained few-shot classification

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SDR-Former: A Siamese Dual-Resolution Transformer for liver lesion classification using 3D multi-phase imaging

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Few-shot semantic segmentation: a review on recent approaches

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Bi-Directional Ensemble Feature Reconstruction Network for Few-Shot Fine-Grained Classification

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Recognizing temporary construction site objects using CLIP-based few-shot learning and multi-modal prototypes

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Multi-branch feature transformation cross-domain few-shot learning for hyperspectral image classification

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Explainability-based knowledge distillation

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A Comprehensive Review of Few-Shot Action Recognition

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Battery health prediction under data scarcity: A cross-domain physics-informed 5-shot framework with GRU-Transformer

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Few-shot classification guided by generalization error bound

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HyperMAML: Few-shot adaptation of deep models with hypernetworks

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Few-shot image classification using graph neural network with fine-grained feature descriptors

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PRSN: Prototype resynthesis network with cross-image semantic alignment for few-shot image classification

Dong, M., Li, F., Li, Z., & Li, Z. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111122 · Cited by 23

Cardiac Disease Classification Using Two-Dimensional Thickness and Few-Shot Learning Based on Magnetic Resonance Imaging Image Segmentation

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Detection of fungal disease in citrus fruit based on hyperspectral imaging

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A Comparative Review of Recent Few-Shot Object Detection AlgorithmsPreprint

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Multimorbidity Content-Based Medical Image Retrieval and Disease Recognition Using Multi-Label Proxy Metric Learning

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Few-Shot Point Cloud Semantic Segmentation via Contrastive Self-Supervision and Multi-Resolution Attention

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Enhancing Few-Shot Image Classification With Cosine Transformer

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CDCNet: Cross-domain few-shot learning with adaptive representation enhancement

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Relation fusion propagation network for transductive few-shot learning

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A simple scheme to amplify inter-class discrepancy for improving few-shot fine-grained image classification

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Deep learning assisted prediction of osteogenic capability of orthopedic implant surfaces based on early cell morphology

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Deep Metric Learning Based on Brownian Covariance Representation for Few-Shot Hyperspectral Image Classification

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Attention-enhanced corn disease diagnosis using few-shot learning and VGG16

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An Anomaly Node Detection Method for Wireless Sensor Networks Based on Deep Metric Learning with Fusion of Spatial–Temporal Features

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High-temperature augmented neighborhood metric learning for cross-domain fault diagnosis with imbalanced data

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A Survey on Stability of Learning with Limited Labelled Data and its Sensitivity to the Effects of Randomness

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A review of few-shot image classification: Approaches, datasets and research trends

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Counter-act against GAN-based attacks: A collaborative learning approach for anti-forensic detection

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Probabilistic deep metric learning for hyperspectral image classification

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Conditional-pooling for improved data transmission

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Meta-collaborative comparison for effective cross-domain few-shot learning

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Predicting the tensile properties of wood plastic composites using material extrusion with meta-based Few-Shot learning

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GLGAT-CFSL: Global-Local Graph Attention Network-Based Cross-Domain Few-Shot Learning for Hyperspectral Image Classification

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A review and systematic guide to counteracting medical data scarcity for AI applications

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Few-shot learning with long-tailed labels

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CDN4: A cross-view Deep Nearest Neighbor Neural Network for fine-grained few-shot classification

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Self-Training-Transductive-Learning Broad Learning System (STTL-BLS): A model for effective and efficient image classification

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Detection of coronary artery disease using a triplet network and hybrid loss function on heart sound signal

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Guiding Prototype Networks with label semantics for few-shot text classification

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Few-shot image classification based on gradual machine learning

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Knowledge-driven prototype refinement for few-shot fine-grained recognition

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Boosting Learning Efficiency in Few-Shot Tasks With Layer-Adaptive PID Control

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Diversified In-domain Synthesis with Efficient Fine-Tuning for Few-Shot Classification

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BiLSTM-TANet: an adaptive diverse scenes model with context embeddings for few-shot learning

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IoT-Enabled Few-Shot Image Generation for Power Scene Defect Detection Based on Self-Attention and Global–Local Fusion

Chen, Y., Yan, Y., Wang, X., & Zheng, Y. (2023) · Sensors · doi:10.3390/s23146531 · Cited by 11

Deep metric learning in projected-hypersphere space

Xu, Y., Chen, Z., & Hu, J. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111245 · Cited by 10

Multi-Stage-Based Siamese Neural Network for Seal Image Recognition

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Transformer gate-based interactive U-Net for hyperspectral and multispectral image fusion

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Deep learning model for hair artifact removal and Mpox skin lesion analysis and detection

Onyema, E. M., Gunapriya, B., Kavin, B. P., Uddin, M., Kumar, P. M., Mazhar, T., & Saeed, M. M. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-05324-2 · Cited by 10

Meta-Transfer Derm-Diagnosis: Exploring Few-Shot Learning and Transfer Learning for Skin Disease Classification in Long-Tail Distribution

Özdemir, Z., Keleş, H. Y., & Tanriöver, Ö. Ö. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3615479 · Cited by 10

Review of dwell-time algorithms for magnetorheological finishing

Xie, M., Cheng, J., Ma, Z., & Huang, S. (2024) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-023-12824-w · Cited by 10

Query-centric distance modulator for few-shot classification

Wu, W., Shao, Y., Gao, C., Xue, J., & Sang, N. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110380 · Cited by 10

A Semi-Supervised Quality Soft Sensing Method Under Multiple Operating Conditions for Complex Product Manufacturing Processes

Peng, K., Wang, J., Yang, H., & Qin, X. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3472794 · Cited by 10

Few-shot SAR image classification via multiple prototypes ensemble

Zhao, Z., Tong, Y., Jia, M., Qiu, Y., Wang, X., & Hei, X. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129989 · Cited by 10

A few-shot learning method for tobacco abnormality identification

Lin, H., Qiang, Z., Tse, R. T., Tang, S.-K., & Pau, G. (2024) · Frontiers in Plant Science · doi:10.3389/fpls.2024.1333236 · Cited by 9

Multi-view aggregation and multi-relation alignment for few-shot fine-grained recognition

Chen, J., Xu, F., Lyu, X., Zeng, T., Li, X., & Chen, S. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127764 · Cited by 9

Few-shot Bearing Fault Diagnosis using Adaptive Detail Convolution and Global KAN-Transformer with Mahalanobis Distance

Zhao, X., Zhao, Z., Cui, X., Yin, J., Cao, J., & Wang, H. (2025) · Journal of Vibration Engineering & Technologies · doi:10.1007/s42417-025-01874-x · Cited by 9

From patch, sample to domain: Capture geometric structures for few-shot learning

Li, Q., Wen, G., & Yang, P. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.110147 · Cited by 9

Few-shot learning for inference in medical imaging with subspace feature representations

Liu, J., Fan, K., Cai, X., & Niranjan, M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0309368 · Cited by 9

Few-shot node classification on attributed networks based on deep metric learning for Cyber–Physical–Social Services

Zhang, G., Zhao, Y., & Wang, J. (2023) · Pattern Recognition Letters · doi:10.1016/j.patrec.2023.08.003 · Cited by 9

DataMap: Dataset transferability map for medical image classification

Du, X., Liu, Z., Feng, Z., & Deng, H. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.110044 · Cited by 8

Self-randomized focuses effectively boost metric-based few-shot classifiers

Li, Z., Liu, Z., Chang, D., Sain, A., Li, X., Ma, Z., Xue, J., & Song, Y.-Z. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111538 · Cited by 8

Progress in applications of self-supervised learning to computer vision in agriculture: A systematic review

Carneiro, G. A., Aubry, T., Cunha, A. M. T. D. S., Radeva, P. I., & Sousa, J. J. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.111134 · Cited by 8

Lightweight Attentive Graph Neural Network with Conditional Random Field for Diagnosis of Anterior Cruciate Ligament Tear

Wang, J., Luo, J., Liang, J., Cao, Y., Feng, J., Tan, L., Wang, Z., Li, J., Hounye, A. H., Hou, M., et al. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-023-00944-4 · Cited by 8

Corn Disease Detection Using Few-Shot Learning Prototypical Network

Rani, R., Sahoo, J., & Bellamkonda, S. (2024) · International Conference on Computational Intelligence and Communication Networks (CICN) · doi:10.1109/cicn63059.2024.10847346 · Cited by 8

AdptGL-CA: Adaptive Global-Local Metric Fusion With Contrastive Attention for Few-Shot Learning

Song, Z., Wang, P., Wang, X., & Zheng, N. (2025) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2025.3550529 · Cited by 8

A Principal Component Analysis-Based Feature Optimization Network for Few-Shot Fine-Grained Image Classification

Wang, M., Zheng, B., Wang, G., Yang, J., Lü, J., & Zhang, W. (2025) · Mathematics · doi:10.3390/math13071098 · Cited by 7

Accelerating ecosystem monitoring through computer vision with deep metric learning

Oba, Y., & Doi, H. (2025) · Ecological Complexity · doi:10.1016/j.ecocom.2025.101124 · Cited by 7

Improved Metric-Learning-Based Recognition Method for Rail Surface State With Small-Sample Data

Yu, H., Peng, C., Liu, J., Zhang, J., & Liu, L. (2023) · IEEE Access · doi:10.1109/access.2023.3347634 · Cited by 7

Meta learning based residual network for industrial production quality prediction with limited data

Shi, Y., Cao, Y., Chen, Y., & Zhang, L. (2024) · Scientific Reports · doi:10.1038/s41598-024-62174-0 · Cited by 7

Anti-Collapse Loss for Deep Metric Learning

Jiang, X., Yao, Y., Dai, X., Shen, F., Nie, L., & Shen, H. T. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3443616 · Cited by 7

Selectively Augmented Attention Network for Few-Shot Image Classification

Li, X., Wang, X., Zhu, R., Ma, Z., Cao, J., & Xue, J. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3480279 · Cited by 7

TKR-FSOD: Fetal Anatomical Structure Few-Shot Detection Utilizing Topological Knowledge Reasoning

Li, X., Tan, Y., Liang, B., Pu, B., Yang, J., Zhao, L., Kong, Y., Yang, L., Zhang, R., Li, H., et al. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3480197 · Cited by 7

TWFSL-MM: Few-Shot Learning using Meta-Learning and Metric-Learning for Disease Detection in Azadirachta Indica

Vidya, H. A., & Murthy, M. S. N. (2025) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.9886 · Cited by 7

SASRNet: Slimming-Assisted Deep Residual Network for Image Steganalysis

Huang, S., Zhang, M., Ke, Y., Di, F., & Kong, Y. (2024) · Computing and Informatics · doi:10.31577/cai_2024_2_295 · Cited by 7

Cross-Domain Adaptation Techniques for Robust Plant Disease Detection: A DANN-CORAL Hybrid Approach

Prasad, P. S., & Senthilrajan, A. (2024) · International Journal of experimental research and review · doi:10.52756/ijerr.2024.v42.007 · Cited by 7

Diversity Vs. Recognizability: Human-Like Generalization in One-Shot Generative Models

Boutin, V., Singhal, L., Thomas, X., & Serre, T. (2022) · neural information processing systems · doi:10.52202/068431-1522 · Cited by 7

Model knowledge-prior embedded subspace learning network for hyperspectral image classification

Nie, X., Xue, Z., & Li, J. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113587 · Cited by 6

Application of Novel SN-1DCNN-LSTM framework in small sample oil and gas pipeline leakage detection

Gao, H., Hao, F., Zhang, Y., Song, X., & Hou, N. (2024) · Franklin Open · doi:10.1016/j.fraope.2024.100073 · Cited by 6

Pine Wilt Disease Segmentation with Deep Metric Learning Species Classification for Early-Stage Disease and Potential False Positive Identification

Thapa, N., Khanal, R., Bhattarai, B., & Lee, J. (2024) · Electronics · doi:10.3390/electronics13101951 · Cited by 6

Class-Irrelevant Feature Removal for Few-Shot Image Classification

Hao, F., Liu, L., Wu, F., Zhang, Q., & Cheng, J. S. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3419787 · Cited by 6

Supervised t-SNE for Metric Learning With Stochastic and Geodesic Distances

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Variational Feature Imitation Conditioned on Visual Descriptions for Few-Shot Fine-Grained Recognition

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