mlsanityData to 7 Oct 2026

An Adaptive Plug-and-Play Network for Few-Shot Learning

arXiv 2302.09326 ↗First posted 18 Feb 2023cs.CVcited by 2

Not on the map. Why?

Abstract (arXiv)

Few-shot learning (FSL) requires a model to classify new samples after learning from only a few samples. While remarkable results are achieved in existing methods, the performance of embedding and metrics determines the upper limit of classification accuracy in FSL. The bottleneck is that deep networks and complex metrics tend to induce overfitting in FSL, making it difficult to further improve the performance. Towards this, we propose plug-and-play model-adaptive resizer (MAR) and adaptive similarity metric (ASM) without any other losses. MAR retains high-resolution details to alleviate the overfitting problem caused by data scarcity, and ASM decouples the relationship between different metrics and then fuses them into an advanced one. Extensive experiments show that the proposed method could boost existing methods on two standard dataset and a fine-grained datasets, and achieve state-of-the-art results on mini-ImageNet and tiered-ImageNet.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 20 of its 31 references that are in the map. First posted on arXiv 18 Feb 2023.

Builds on

20 of 31 references.

References · 20

  1. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  2. 2016Matching Networks for One Shot Learningcited by 2,246
  3. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,139
  4. 2019A Closer Look at Few-shot Classificationcited by 679
  5. 2018Meta-Learning for Semi-Supervised Few-Shot Classificationcited by 456
  6. 2018Few-Shot Learning via Embedding Adaptation with Set-to-Set Functionscited by 293
  7. 2020Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learningcited by 179
  8. 2020CrossTransformers: spatially-aware few-shot transfercited by 174
  9. 2021Free Lunch for Few-shot Learning: Distribution Calibrationcited by 165
  10. 2019Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learningcited by 121
  11. 2020Few-Shot Classification with Feature Map Reconstruction Networkscited by 116
  12. 2018Task-Driven Super Resolution: Object Detection in Low-resolution Imagescited by 52
8 more
  1. 2022Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classificationcited by 50
  2. 2019Few-Shot Learning with Localization in Realistic Settingscited by 34
  3. 2020BSNet: Bi-Similarity Network for Few-shot Fine-grained Image Classificationcited by 30
  4. 2022Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learningcited by 27
  5. 2021Binocular Mutual Learning for Improving Few-shot Classificationcited by 26
  6. 2020The Rate-Distortion-Accuracy Tradeoff: JPEG Case Studycited by 22
  7. 2020Asymmetric Distribution Measure for Few-shot Learningcited by 18
  8. 2018Power Normalizing Second-order Similarity Network for Few-shot Learningcited by 13

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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Slot Attention-based Feature Filtering for Few-Shot Learningcited by 1
  2. 2025Stochastic-based Patch Filtering for Few-Shot Learningcited by 0