mlsanityData to 7 Oct 2026

ECKPN: Explicit Class Knowledge Propagation Network for Transductive Few-shot Learning

arXiv 2106.08523 ↗First posted 16 Jun 2021cs.CV cs.AIcited by 11

Not on the map. Why?

Abstract (arXiv)

Recently, the transductive graph-based methods have achieved great success in the few-shot classification task. However, most existing methods ignore exploring the class-level knowledge that can be easily learned by humans from just a handful of samples. In this paper, we propose an Explicit Class Knowledge Propagation Network (ECKPN), which is composed of the comparison, squeeze and calibration modules, to address this problem. Specifically, we first employ the comparison module to explore the pairwise sample relations to learn rich sample representations in the instance-level graph. Then, we squeeze the instance-level graph to generate the class-level graph, which can help obtain the class-level visual knowledge and facilitate modeling the relations of different classes. Next, the calibration module is adopted to characterize the relations of the classes explicitly to obtain the more discriminative class-level knowledge representations. Finally, we combine the class-level knowledge with the instance-level sample representations to guide the inference of the query samples. We conduct extensive experiments on four few-shot classification benchmarks, and the experimental results show that the proposed ECKPN significantly outperforms the state-of-the-art methods.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 38 of its 53 references that are in the map. First posted on arXiv 16 Jun 2021.

Builds on

38 of 53 references.

References · 38

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  4. 2017Graph Attention Networkscited by 8,854
  5. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  6. 2017Inductive Representation Learning on Large Graphscited by 6,284
  7. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  8. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  9. 2013Visualizing and Understanding Convolutional Networkscited by 2,278
  10. 2016Matching Networks for One Shot Learningcited by 2,246
  11. 2018Group Normalizationcited by 1,514
  12. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,139
26 more
  1. 2018On First-Order Meta-Learning Algorithmscited by 1,003
  2. 2018Meta-Learning with Latent Embedding Optimizationcited by 476
  3. 2019Meta-Learning with Differentiable Convex Optimizationcited by 461
  4. 2018Meta-Learning for Semi-Supervised Few-Shot Classificationcited by 456
  5. 2018TADAM: Task dependent adaptive metric for improved few-shot learningcited by 408
  6. 2018Meta-learning with differentiable closed-form solverscited by 395
  7. 2018Dynamic Few-Shot Visual Learning without Forgettingcited by 385
  8. 2017Meta-SGD: Learning to Learn Quickly for Few-Shot Learningcited by 377
  9. 2017Few-Shot Learning with Graph Neural Networkscited by 324
  10. 2018Meta-Transfer Learning for Few-Shot Learningcited by 306
  11. 2014Caffe: Convolutional Architecture for Fast Feature Embeddingcited by 287
  12. 2018Probabilistic Model-Agnostic Meta-Learningcited by 254
  13. 2018Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learningcited by 208
  14. 2018Bayesian Model-Agnostic Meta-Learningcited by 194
  15. 2018Low-Shot Learning from Imaginary Datacited by 181
  16. 2019Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learningcited by 121
  17. 2019Edge-labeling Graph Neural Network for Few-shot Learningcited by 114
  18. 2020Adversarial Feature Hallucination Networks for Few-Shot Learningcited by 110
  19. 2019Finding Task-Relevant Features for Few-Shot Learning by Category Traversalcited by 101
  20. 2019TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learningcited by 90
  21. 2020DPGN: Distribution Propagation Graph Network for Few-shot Learningcited by 79
  22. 2019Meta-Learning of Neural Architectures for Few-Shot Learningcited by 73
  23. 2019Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot Learningcited by 69
  24. 2019Few-Shot Learning with Embedded Class Models and Shot-Free Meta Trainingcited by 57
  25. 2019A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classificationcited by 45
  26. 2019Learning to Propagate for Graph Meta-Learningcited by 29

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 3; 2023: 5; 2024: 1; 2025: 1.

11 papers.

  1. 2023Transductive Few-shot Learning with Prototype-based Label Propagation by Iterative Graph Refinementcited by 30
  2. 2023Boosting Few-shot Action Recognition with Graph-guided Hybrid Matchingcited by 24
  3. 2022EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredientscited by 15
  4. 2021Prototype Completion for Few-Shot Learningcited by 7
  5. 2025Analytical Survey of Learning with Low-Resource Data: From Analysis to Investigationcited by 5
  6. 2023Constructing Sample-to-Class Graph for Few-Shot Class-Incremental Learningcited by 2
  7. 2022Learning Compositional Representations for Effective Low-Shot Generalizationcited by 1
  8. 2024Harnessing the Power of Local Representations for Few-Shot Classificationcited by 1
  9. 2022Fine-grained Few-shot Recognition by Deep Object Parsingcited by 0
  10. 2023Two-level Graph Network for Few-Shot Class-Incremental Learningcited by 0
  11. 2023Multi-level Relation Learning for Cross-domain Few-shot Hyperspectral Image Classificationcited by 0