Data to 7 Oct 2026

DPGN: Distribution Propagation Graph Network for Few-shot Learning

arXiv 2003.14247 ↗First posted 31 Mar 2020cs.CVcited by 79

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Abstract (arXiv)

Most graph-network-based meta-learning approaches model instance-level relation of examples. We extend this idea further to explicitly model the distribution-level relation of one example to all other examples in a 1-vs-N manner. We propose a novel approach named distribution propagation graph network (DPGN) for few-shot learning. It conveys both the distribution-level relations and instance-level relations in each few-shot learning task. To combine the distribution-level relations and instance-level relations for all examples, we construct a dual complete graph network which consists of a point graph and a distribution graph with each node standing for an example. Equipped with dual graph architecture, DPGN propagates label information from labeled examples to unlabeled examples within several update generations. In extensive experiments on few-shot learning benchmarks, DPGN outperforms state-of-the-art results by a large margin in 5% ~ 12% under supervised setting and 7% ~ 13% under semi-supervised setting. Code will be released.

Key facts. Cited by 79 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Mar 2020.

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Citing papers per year: 2021: 25; 2022: 31; 2023: 10; 2024: 6; 2025: 7.

79 papers.

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2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
2022A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunitiescited by 162
2022Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusioncited by 96
2022Bridging the Domain Gap for Multi-Agent Perceptioncited by 60
2022A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspectivecited by 56
2021Property-Aware Relation Networks for Few-Shot Molecular Property Predictioncited by 41
2023VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPscited by 34
2024Improving Diffusion-Based Image Synthesis with Context Predictioncited by 32
2021Binocular Mutual Learning for Improving Few-shot Classificationcited by 26
2022Realistic Evaluation of Transductive Few-Shot Learningcited by 26
2021Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developmentscited by 23
2021Transductive Few-Shot Classification on the Oblique Manifoldcited by 23

38 more

2021Meta Navigator: Search for a Good Adaptation Policy for Few-shot Learningcited by 19
2022Attribute Prototype Network for Any-Shot Learningcited by 19
2025Geo-R1: Improving Few-Shot Geospatial Referring Expression Understanding with Reinforcement Fine-Tuningcited by 19
2021Contrastive Prototype Learning with Augmented Embeddings for Few-Shot Learningcited by 15
2021Hybrid Graph Neural Networks for Few-Shot Learningcited by 15
2022EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredientscited by 15
2023Individual and Structural Graph Information Bottlenecks for Out-of-Distribution Generalizationcited by 15
2022Multimodal Prototype-Enhanced Network for Few-Shot Action Recognitioncited by 13
2022Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image Classificationcited by 12
2024Binding-Adaptive Diffusion Models for Structure-Based Drug Designcited by 12
2021MetaNODE: Prototype Optimization as a Neural ODE for Few-Shot Learningcited by 11
2021Few-Shot Learning by Integrating Spatial and Frequency Representationcited by 11
2021ECKPN: Explicit Class Knowledge Propagation Network for Transductive Few-shot Learningcited by 11
2021What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Datacited by 10
2022Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classificationcited by 10
2023PVG: Progressive Vision Graph for Vision Recognitioncited by 10
2021Graph Representation Learning for Road Type Classificationcited by 8
2022Powering Finetuning in Few-Shot Learning: Domain-Agnostic Bias Reduction with Selected Samplingcited by 8
2023Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalizationcited by 8
2021Prototype Completion for Few-Shot Learningcited by 7
2022Transductive Decoupled Variational Inference for Few-Shot Classificationcited by 7
2021Information Symmetry Matters: A Modal-Alternating Propagation Network for Few-Shot Learningcited by 6
2021MHFC: Multi-Head Feature Collaboration for Few-Shot Learningcited by 6
2022Automatic Relation-aware Graph Network Proliferationcited by 6
2022Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptilecited by 6
2022Few-shot Image Generation Using Discrete Content Representationcited by 6
2024BECLR: Batch Enhanced Contrastive Few-Shot Learningcited by 6
2021Progressive Co-Attention Network for Fine-grained Visual Classificationcited by 5
2021MDFM: Multi-Decision Fusing Model for Few-Shot Learningcited by 5
2022GCT: Graph Co-Training for Semi-Supervised Few-Shot Learningcited by 5
2022Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learningcited by 5
2025Analytical Survey of Learning with Low-Resource Data: From Analysis to Investigationcited by 5
2021Local Propagation for Few-Shot Learningcited by 4
2021Transductive Few-Shot Learning: Clustering is All You Need?cited by 4
2022Self-Attention Message Passing for Contrastive Few-Shot Learningcited by 4
2022Few-Shot Object Detection by Knowledge Distillation Using Bag-of-Visual-Words Representationscited by 3
2025HGFormer: Topology-Aware Vision Transformer with HyperGraph Learningcited by 3
2022Spectral Propagation Graph Network for Few-shot Time Series Classificationcited by 2

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