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Few-Shot Classification with Feature Map Reconstruction Networks

arXiv 2012.01506 ↗First posted 2 Dec 2020cs.CV cs.LGcited by 116

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Abstract (arXiv)

In this paper we reformulate few-shot classification as a reconstruction problem in latent space. The ability of the network to reconstruct a query feature map from support features of a given class predicts membership of the query in that class. We introduce a novel mechanism for few-shot classification by regressing directly from support features to query features in closed form, without introducing any new modules or large-scale learnable parameters. The resulting Feature Map Reconstruction Networks are both more performant and computationally efficient than previous approaches. We demonstrate consistent and substantial accuracy gains on four fine-grained benchmarks with varying neural architectures. Our model is also competitive on the non-fine-grained mini-ImageNet and tiered-ImageNet benchmarks with minimal bells and whistles.

Key facts. Cited by 116 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Dec 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 14; 2022: 29; 2023: 22; 2024: 23; 2025: 17; 2026: 11.

116 papers.

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  1. 2022A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunitiescited by 162
  2. 2021Relational Embedding for Few-Shot Classificationcited by 74
  3. 2021Do Different Tracking Tasks Require Different Appearance Models?cited by 62
  4. 2022Few-shot Object Counting with Similarity-Aware Feature Enhancementcited by 62
  5. 2021Rectifying the Shortcut Learning of Background for Few-Shot Learningcited by 51
  6. 2022Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classificationcited by 50
  7. 2022Generating Representative Samples for Few-Shot Classificationcited by 39
  8. 2022Rethinking Generalization in Few-Shot Classificationcited by 36
  9. 2022Boosting Few-shot Fine-grained Recognition with Background Suppression and Foreground Alignmentcited by 29
  10. 2022Inductive and Transductive Few-Shot Video Classification via Appearance and Temporal Alignmentscited by 22
  11. 2021LibFewShot: A Comprehensive Library for Few-shot Learningcited by 21
  12. 2022Task Discrepancy Maximization for Fine-grained Few-Shot Classificationcited by 21
38 more
  1. 2023Context-Aware Meta-Learningcited by 20
  2. 2023Does Few-shot Learning Suffer from Backdoor Attacks?cited by 19
  3. 2021Learning to Affiliate: Mutual Centralized Learning for Few-shot Classificationcited by 18
  4. 2024Compositional Few-Shot Class-Incremental Learningcited by 18
  5. 2022Few-Shot Learning Meets Transformer: Unified Query-Support Transformers for Few-Shot Classificationcited by 16
  6. 2021Adaptive Poincar'e Point to Set Distance for Few-Shot Classificationcited by 15
  7. 2021Hybrid Graph Neural Networks for Few-Shot Learningcited by 15
  8. 2023Dual Adaptive Representation Alignment for Cross-domain Few-shot Learningcited by 15
  9. 2023DETA: Denoised Task Adaptation for Few-Shot Learningcited by 13
  10. 2021The Role of Global Labels in Few-Shot Classification and How to Infer Themcited by 12
  11. 2021Disentangled Feature Representation for Few-shot Image Classificationcited by 12
  12. 2022Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image Classificationcited by 12
  13. 2024Few-shot Learner Parameterization by Diffusion Time-stepscited by 11
  14. 2022Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classificationcited by 10
  15. 2022An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learningcited by 10
  16. 2024Boosting Few-Shot Learning via Attentive Feature Regularizationcited by 10
  17. 2021DMN4: Few-shot Learning via Discriminative Mutual Nearest Neighbor Neural Networkcited by 9
  18. 2022CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classificationcited by 9
  19. 2022Learning Cross-Image Object Semantic Relation in Transformer for Few-Shot Fine-Grained Image Classificationcited by 9
  20. 2021Revisiting Local Descriptor for Improved Few-Shot Classificationcited by 8
  21. 2024Efficient Prompt Tuning of Large Vision-Language Model for Fine-Grained Ship Classificationcited by 8
  22. 2022Transductive Decoupled Variational Inference for Few-Shot Classificationcited by 7
  23. 2022RankDNN: Learning to Rank for Few-shot Learningcited by 7
  24. 2023Semantic-Aware Graph Matching Mechanism for Multi-Label Image Recognitioncited by 7
  25. 2023Introspective Deep Metric Learningcited by 7
  26. 2024A Survey of Few-Shot Learning for Biomedical Time Seriescited by 7
  27. 2024Making Large Vision Language Models to be Good Few-shot Learnerscited by 7
  28. 2025VT-FSL: Bridging Vision and Text with LLMs for Few-Shot Learningcited by 7
  29. 2021Tensor feature hallucination for few-shot learningcited by 6
  30. 2023ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot Learningcited by 6
  31. 2023Robust Saliency-Aware Distillation for Few-shot Fine-grained Visual Recognitioncited by 6
  32. 2024MetaCoCo: A New Few-Shot Classification Benchmark with Spurious Correlationcited by 5
  33. 2024Learning to Obstruct Few-Shot Image Classification over Restricted Classescited by 5
  34. 2021Learning Instance and Task-Aware Dynamic Kernels for Few Shot Learningcited by 4
  35. 2022Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learningcited by 4
  36. 2022PatchMix Augmentation to Identify Causal Features in Few-shot Learningcited by 4
  37. 2023Clustered-patch Element Connection for Few-shot Learningcited by 4
  38. 2023Task-Oriented Channel Attention for Fine-Grained Few-Shot Classificationcited by 4

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