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Few-Shot Learning with Graph Neural Networks

arXiv 1711.04043 ↗First posted 10 Nov 2017stat.ML cs.LGcited by 324

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Abstract (arXiv)

We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not. By assimilating generic message-passing inference algorithms with their neural-network counterparts, we define a graph neural network architecture that generalizes several of the recently proposed few-shot learning models. Besides providing improved numerical performance, our framework is easily extended to variants of few-shot learning, such as semi-supervised or active learning, demonstrating the ability of graph-based models to operate well on 'relational' tasks.

Key facts. Cited by 324 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Nov 2017.

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Citing papers per year: 2021: 113; 2022: 82; 2023: 57; 2024: 40; 2025: 24; 2026: 8.

324 papers.

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  1. 2022Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classificationcited by 310
  2. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 260
  3. 2023NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classificationcited by 207
  4. 2021Hypercorrelation Squeeze for Few-Shot Segmentationcited by 193
  5. 2021Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networkscited by 182
  6. 2021FSCE: Few-Shot Object Detection via Contrastive Proposal Encodingcited by 168
  7. 2022A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunitiescited by 162
  8. 2021LaneRCNN: Distributed Representations for Graph-Centric Motion Forecastingcited by 112
  9. 2021Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learningcited by 112
  10. 2021SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networkscited by 108
  11. 2021Graph Neural Networks in Network Neurosciencecited by 91
  12. 2021Few-Shot Graph Learning for Molecular Property Predictioncited by 81
38 more
  1. 2022FS6D: Few-Shot 6D Pose Estimation of Novel Objectscited by 81
  2. 2021Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learningcited by 72
  3. 2023Advances and Challenges in Meta-Learning: A Technical Reviewcited by 65
  4. 2021Cross-Domain Few-Shot Classification via Adversarial Task Augmentationcited by 63
  5. 2022Transferability in Deep Learning: A Surveycited by 57
  6. 2022Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learningcited by 57
  7. 2022A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspectivecited by 56
  8. 2023StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learningcited by 53
  9. 2021Pareto Self-Supervised Training for Few-Shot Learningcited by 52
  10. 2021Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencodercited by 51
  11. 2021Shallow Bayesian Meta Learning for Real-World Few-Shot Recognitioncited by 48
  12. 2021Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Datacited by 48
  13. 2022Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvementcited by 48
  14. 2021Meta-Learning with Fewer Tasks through Task Interpolationcited by 45
  15. 2021Generalization Bounds For Meta-Learning: An Information-Theoretic Analysiscited by 45
  16. 2021Boost then Convolve: Gradient Boosting Meets Graph Neural Networkscited by 42
  17. 2021Accelerating Large Scale Real-Time GNN Inference using Channel Pruningcited by 41
  18. 2021Property-Aware Relation Networks for Few-Shot Molecular Property Predictioncited by 41
  19. 2023Towards Understanding the Generalization of Graph Neural Networkscited by 41
  20. 2021p-Laplacian Based Graph Neural Networkscited by 39
  21. 2021Visual Camera Re-Localization Using Graph Neural Networks and Relative Pose Supervisioncited by 37
  22. 2022Adversarial Feature Augmentation for Cross-domain Few-shot Classificationcited by 36
  23. 2023GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learningcited by 32
  24. 2022Few-Shot Classification with Contrastive Learningcited by 31
  25. 2022Unlearning Graph Classifiers with Limited Data Resourcescited by 31
  26. 2023Transductive Few-shot Learning with Prototype-based Label Propagation by Iterative Graph Refinementcited by 30
  27. 2021A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Predictioncited by 28
  28. 2021Learning Implicit Temporal Alignment for Few-shot Video Classificationcited by 27
  29. 2021Dual Graph Convolutional Networks with Transformer and Curriculum Learning for Image Captioningcited by 27
  30. 2021Ranking Distance Calibration for Cross-Domain Few-Shot Learningcited by 27
  31. 2021Distilling Holistic Knowledge with Graph Neural Networkscited by 26
  32. 2023Prototype Adaption and Projection for Few- and Zero-shot 3D Point Cloud Semantic Segmentationcited by 26
  33. 2021Data-Efficient GAN Training Beyond (Just) Augmentations: A Lottery Ticket Perspectivecited by 25
  34. 2022Source-Free Progressive Graph Learning for Open-Set Domain Adaptationcited by 25
  35. 2022Matching Feature Sets for Few-Shot Image Classificationcited by 25
  36. 2023Meta-learning approaches for few-shot learning: A survey of recent advancescited by 25
  37. 2021Exploring Task Difficulty for Few-Shot Relation Extractioncited by 24
  38. 2023Boosting Few-shot Action Recognition with Graph-guided Hybrid Matchingcited by 24

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