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SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning

arXiv 1911.04623 ↗First posted 12 Nov 2019cs.CVcited by 195

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Abstract (arXiv)

Few-shot learners aim to recognize new object classes based on a small number of labeled training examples. To prevent overfitting, state-of-the-art few-shot learners use meta-learning on convolutional-network features and perform classification using a nearest-neighbor classifier. This paper studies the accuracy of nearest-neighbor baselines without meta-learning. Surprisingly, we find simple feature transformations suffice to obtain competitive few-shot learning accuracies. For example, we find that a nearest-neighbor classifier used in combination with mean-subtraction and L2-normalization outperforms prior results in three out of five settings on the miniImageNet dataset.

Key facts. Cited by 195 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Nov 2019.

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Citing papers per year: 2021: 44; 2022: 49; 2023: 45; 2024: 25; 2025: 18; 2026: 14.

195 papers.

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  1. 2023StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learnerscited by 177
  2. 2024Multimodal Whole Slide Foundation Model for Pathologycited by 162
  3. 2023BioCLIP: A Vision Foundation Model for the Tree of Lifecited by 161
  4. 2021The Surprising Effectiveness of Representation Learning for Visual Imitationcited by 154
  5. 2022Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Differencecited by 126
  6. 2021Using Shape to Categorize: Low-Shot Learning with an Explicit Shape Biascited by 118
  7. 2021Generalized Few-Shot Object Detection without Forgettingcited by 88
  8. 2022Few-Shot Class-Incremental Learning by Sampling Multi-Phase Taskscited by 88
  9. 2022Learning from Few Examples: A Summary of Approaches to Few-Shot Learningcited by 86
  10. 2021Relational Embedding for Few-Shot Classificationcited by 74
  11. 2021CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learningcited by 64
  12. 2022Test-Time Adaptation via Self-Training with Nearest Neighbor Informationcited by 64
38 more
  1. 2021Personalized Federated Learning through Local Memorizationcited by 57
  2. 2025BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive Learningcited by 50
  3. 2021Shallow Bayesian Meta Learning for Real-World Few-Shot Recognitioncited by 48
  4. 2023A General-Purpose Self-Supervised Model for Computational Pathologycited by 42
  5. 2024RudolfV: A Foundation Model by Pathologists for Pathologistscited by 41
  6. 2022Generating Representative Samples for Few-Shot Classificationcited by 39
  7. 2023MoLo: Motion-augmented Long-short Contrastive Learning for Few-shot Action Recognitioncited by 37
  8. 2021Few-shot Intent Classification and Slot Filling with Retrieved Examplescited by 35
  9. 2022Handcrafted Histological Transformer (H2T): Unsupervised Representation of Whole Slide Imagescited by 32
  10. 2023Learning Visual Representations via Language-Guided Samplingcited by 32
  11. 2024Transductive Zero-Shot and Few-Shot CLIPcited by 32
  12. 2022Few-Shot Classification with Contrastive Learningcited by 31
  13. 2023Transductive Few-shot Learning with Prototype-based Label Propagation by Iterative Graph Refinementcited by 30
  14. 2022Realistic Evaluation of Transductive Few-Shot Learningcited by 27
  15. 2024MVP-Shot: Multi-Velocity Progressive-Alignment Framework for Few-Shot Action Recognitioncited by 25
  16. 2021Procrustean Training for Imbalanced Deep Learningcited by 24
  17. 2022HyperShot: Few-Shot Learning by Kernel HyperNetworkscited by 24
  18. 2022MetaAudio: A Few-Shot Audio Classification Benchmarkcited by 24
  19. 2024Fine-Tuning is Fine, if Calibratedcited by 24
  20. 2021Transductive Few-Shot Classification on the Oblique Manifoldcited by 23
  21. 2021How to Train Your MAML to Excel in Few-Shot Classificationcited by 22
  22. 2022Task Discrepancy Maximization for Fine-grained Few-Shot Classificationcited by 21
  23. 2022FHIST: A Benchmark for Few-shot Classification of Histological Imagescited by 20
  24. 2022Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroidcited by 20
  25. 2021Few-Shot Learning with Class Imbalancecited by 18
  26. 2021Reinforced Attention for Few-Shot Learning and Beyondcited by 18
  27. 2023Learning to Learn from APIs: Black-Box Data-Free Meta-Learningcited by 18
  28. 2022HyperMAML: Few-Shot Adaptation of Deep Models with Hypernetworkscited by 17
  29. 2021Few-Shot Learning with a Strong Teachercited by 16
  30. 2022EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredientscited by 16
  31. 2022Towards better understanding and better generalization of few-shot classification in histology images with contrastive learningcited by 16
  32. 2021Rich Semantics Improve Few-shot Learningcited by 15
  33. 2021A Closer Look at Prototype Classifier for Few-shot Image Classificationcited by 15
  34. 2021Adaptive Poincar'e Point to Set Distance for Few-Shot Classificationcited by 15
  35. 2022POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samplescited by 15
  36. 2021Multilingual and cross-lingual document classification: A meta-learning approachcited by 14
  37. 2021Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learningcited by 14
  38. 2023CDFSL-V: Cross-Domain Few-Shot Learning for Videoscited by 13

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