mlsanityData to 14 Oct 2026

Transductive Few-shot Learning with Prototype-based Label Propagation by Iterative Graph Refinement

arXiv 2304.11598 ↗First posted 23 Apr 2023cs.CV cs.AI cs.LGcited by 30

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Abstract (arXiv)

Few-shot learning (FSL) is popular due to its ability to adapt to novel classes. Compared with inductive few-shot learning, transductive models typically perform better as they leverage all samples of the query set. The two existing classes of methods, prototype-based and graph-based, have the disadvantages of inaccurate prototype estimation and sub-optimal graph construction with kernel functions, respectively. In this paper, we propose a novel prototype-based label propagation to solve these issues. Specifically, our graph construction is based on the relation between prototypes and samples rather than between samples. As prototypes are being updated, the graph changes. We also estimate the label of each prototype instead of considering a prototype be the class centre. On mini-ImageNet, tiered-ImageNet, CIFAR-FS and CUB datasets, we show the proposed method outperforms other state-of-the-art methods in transductive FSL and semi-supervised FSL when some unlabeled data accompanies the novel few-shot task.

Key facts. Cited by 30 papers in the map (data through 14 Oct 2026). Builds on 45 of its 71 references that are in the map. First posted on arXiv 23 Apr 2023.

Builds on

45 of 71 references.

References · 45

  1. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  3. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  4. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  5. 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,316
  6. 2016Matching Networks for One Shot Learningcited by 2,255
  7. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,143
  8. 2019A Closer Look at Few-shot Classificationcited by 684
  9. 2018Meta-Learning with Latent Embedding Optimizationcited by 476
  10. 2019Meta-Learning with Differentiable Convex Optimizationcited by 461
  11. 2018Meta-Learning for Semi-Supervised Few-Shot Classificationcited by 456
  12. 2018TADAM: Task dependent adaptive metric for improved few-shot learningcited by 408
33 more
  1. 2017Few-Shot Learning with Graph Neural Networkscited by 324
  2. 2019A Baseline for Few-Shot Image Classificationcited by 298
  3. 2018Few-Shot Learning via Embedding Adaptation with Set-to-Set Functionscited by 294
  4. 2019Cross Attention Network for Few-shot Classificationcited by 219
  5. 2018Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learningcited by 211
  6. 2019SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learningcited by 195
  7. 2019Charting the Right Manifold: Manifold Mixup for Few-shot Learningcited by 136
  8. 2019Edge-labeling Graph Neural Network for Few-shot Learningcited by 114
  9. 2019Prototype Rectification for Few-Shot Learningcited by 97
  10. 2020Laplacian Regularized Few-Shot Learningcited by 92
  11. 2019Learning to Self-Train for Semi-Supervised Few-Shot Classificationcited by 83
  12. 2020Embedding Propagation: Smoother Manifold for Few-Shot Classificationcited by 80
  13. 2020Leveraging the Feature Distribution in Transfer-based Few-Shot Learningcited by 74
  14. 2019Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot Learningcited by 69
  15. 2019Transductive Episodic-Wise Adaptive Metric for Few-Shot Learningcited by 67
  16. 2020Instance Credibility Inference for Few-Shot Learningcited by 57
  17. 2020Empirical Bayes Transductive Meta-Learning with Synthetic Gradientscited by 48
  18. 2020Iterative label cleaning for transductive and semi-supervised few-shot learningcited by 40
  19. 2020TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classificationcited by 30
  20. 2020Power Normalizations in Fine-grained Image, Few-shot Image and Graph Classificationcited by 27
  21. 2021Few-shot Keypoint Detection with Uncertainty Learning for Unseen Speciescited by 27
  22. 2022Realistic Evaluation of Transductive Few-Shot Learningcited by 27
  23. 2021Transductive Few-Shot Classification on the Oblique Manifoldcited by 23
  24. 2022Temporal-Viewpoint Transportation Plan for Skeletal Few-shot Action Recognitioncited by 22
  25. 2021Meta-Learning for Multi-Label Few-Shot Classificationcited by 21
  26. 2022Uncertainty-DTW for Time Series and Sequencescited by 21
  27. 2021Reinforced Attention for Few-Shot Learning and Beyondcited by 18
  28. 2021Manifold Learning Benefits GANscited by 14
  29. 2022Multi-level Second-order Few-shot Learningcited by 12
  30. 2021ECKPN: Explicit Class Knowledge Propagation Network for Transductive Few-shot Learningcited by 11
  31. 2022Time-rEversed diffusioN tEnsor Transformer: A new TENET of Few-Shot Object Detectioncited by 10
  32. 2020Improving Few-shot Learning by Spatially-aware Matching and CrossTransformercited by 2
  33. 2021Towards a Robust Differentiable Architecture Search under Label Noisecited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 9; 2024: 8; 2025: 9; 2026: 4.

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  1. 2023A General-Purpose Self-Supervised Model for Computational Pathologycited by 42
  2. 2023MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learningcited by 40
  3. 2024Transductive Zero-Shot and Few-Shot CLIPcited by 32
  4. 2024Visual Prompting for Generalized Few-shot Segmentation: A Multi-scale Approachcited by 15
  5. 2024Few-Shot Recognition via Stage-Wise Retrieval-Augmented Finetuningcited by 15
  6. 2025Learning Clustering-based Prototypes for Compositional Zero-shot Learningcited by 14
  7. 2023Traffic Forecasting on New Roads Using Spatial Contrastive Pre-Training (SCPT)cited by 11
  8. 2024Meet JEANIE: a Similarity Measure for 3D Skeleton Sequences via Temporal-Viewpoint Alignmentcited by 10
  9. 2024Efficient and Context-Aware Label Propagation for Zero-/Few-Shot Training-Free Adaptation of Vision-Language Modelcited by 8
  10. 2023Adaptive manifold for imbalanced transductive few-shot learningcited by 4
  11. 2023PrototypeFormer: Learning to Explore Prototype Relationships for Few-shot Image Classificationcited by 3
  12. 2024ProCNS: Progressive Prototype Calibration and Noise Suppression for Weakly-Supervised Medical Image Segmentationcited by 3
18 more
  1. 2023Exploiting Field Dependencies for Learning on Categorical Datacited by 2
  2. 2025Cross-Domain Few-Shot Learning with Coalescent Projections and Latent Space Reservationcited by 2
  3. 2026SpurAudio: A Benchmark for Studying Shortcut Learning in Few-Shot Audio Classificationcited by 2
  4. 2024MICM: Rethinking Unsupervised Pretraining for Enhanced Few-shot Learningcited by 1
  5. 2024UNEM: UNrolled Generalized EM for Transductive Few-Shot Learningcited by 1
  6. 2025Mitigating Domain Shift in Federated Learning via Intra- and Inter-Domain Prototypescited by 1
  7. 2026Make It Up: Fake Images, Real Gains in Generalized Few-shot Semantic Segmentationcited by 1
  8. 2023Few-shot Image Classification based on Gradual Machine Learningcited by 0
  9. 2023Language-Guided Reinforcement Learning for Hard Attention in Few-Shot Learningcited by 0
  10. 2023Robust Transductive Few-shot Learning via Joint Message Passing and Prototype-based Soft-label Propagationcited by 0
  11. 2025Semantically Guided Dynamic Visual Prototype Refinement for Compositional Zero-Shot Learningcited by 0
  12. 2025Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learningcited by 0
  13. 2025Unbiased Max-Min Embedding Classification for Transductive Few-Shot Learning: Clustering and Classification Are All You Needcited by 0
  14. 2025Hierarchical Relation-augmented Representation Generalization for Few-shot Action Recognitioncited by 0
  15. 2025Color as the Impetus: Transforming Few-Shot Learnercited by 0
  16. 2025Object-Centric Cropping for Visual Few-Shot Classificationcited by 0
  17. 2026Learning from Limited Labels: Transductive Graph Label Propagation for Indian Music Analysiscited by 0
  18. 2026Rethinking Test-Time Training: Tilting The Latent Distribution For Few-Shot Source-Free Adaptationcited by 0