mlsanityData to 14 Oct 2026

Multi-level Metric Learning for Few-shot Image Recognition

arXiv 2103.11383 ↗First posted 21 Mar 2021cs.CVcited by 8

Not on the map. Why?

Abstract (arXiv)

Few-shot learning is devoted to training a model on few samples. Most of these approaches learn a model based on a pixel-level or global-level feature representation. However, using global features may lose local information, and using pixel-level features may lose the contextual semantics of the image. Moreover, such works can only measure the relations between them on a single level, which is not comprehensive and effective. And if query images can simultaneously be well classified via three distinct level similarity metrics, the query images within a class can be more tightly distributed in a smaller feature space, generating more discriminative feature maps. Motivated by this, we propose a novel Part-level Embedding Adaptation with Graph (PEAG) method to generate task-specific features. Moreover, a Multi-level Metric Learning (MML) method is proposed, which not only calculates the pixel-level similarity but also considers the similarity of part-level features and global-level features. Extensive experiments on popular few-shot image recognition datasets prove the effectiveness of our method compared with the state-of-the-art methods. Our code is available at urlhttps://github.com/chenhaoxing/M2L.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 32 of its 50 references that are in the map. First posted on arXiv 21 Mar 2021.

Builds on

32 of 50 references.

References · 32

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  4. 2017Squeeze-and-Excitation Networkscited by 4,789
  5. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  6. 2018CBAM: Convolutional Block Attention Modulecited by 2,477
  7. 2016Matching Networks for One Shot Learningcited by 2,255
  8. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,143
  9. 2019A Closer Look at Few-shot Classificationcited by 684
  10. 2019Meta-Learning with Differentiable Convex Optimizationcited by 461
  11. 2018Meta-Learning for Semi-Supervised Few-Shot Classificationcited by 456
  12. 2018TADAM: Task dependent adaptive metric for improved few-shot learningcited by 408
20 more
  1. 2018Meta-learning with differentiable closed-form solverscited by 397
  2. 2017Few-Shot Learning with Graph Neural Networkscited by 324
  3. 2018Meta-Transfer Learning for Few-Shot Learningcited by 306
  4. 2019A Baseline for Few-Shot Image Classificationcited by 298
  5. 2018Few-Shot Learning via Embedding Adaptation with Set-to-Set Functionscited by 294
  6. 2020Negative Margin Matters: Understanding Margin in Few-shot Classificationcited by 129
  7. 2019Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learningcited by 121
  8. 2019Finding Task-Relevant Features for Few-Shot Learning by Category Traversalcited by 101
  9. 2019Meta-Learning with Warped Gradient Descentcited by 98
  10. 2019Episode-based Prototype Generating Network for Zero-Shot Learningcited by 66
  11. 2019Associative Alignment for Few-shot Image Classificationcited by 58
  12. 2019TransMatch: A Transfer-Learning Scheme for Semi-Supervised Few-Shot Learningcited by 39
  13. 2017Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognitioncited by 37
  14. 2019Low-Rank Pairwise Alignment Bilinear Network For Few-Shot Fine-Grained Image Classificationcited by 36
  15. 2019Learning to Forget for Meta-Learningcited by 35
  16. 2018Piecewise classifier mappings: Learning fine-grained learners for novel categories with few examplescited by 27
  17. 2020Semi-supervised Learning for Few-shot Image-to-Image Translationcited by 25
  18. 2019A Two-Stage Approach to Few-Shot Learning for Image Recognitioncited by 18
  19. 2020Asymmetric Distribution Measure for Few-shot Learningcited by 18
  20. 2020Multi-scale Adaptive Task Attention Network for Few-Shot Learningcited by 10

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 5; 2023: 1; 2024: 0; 2025: 0; 2026: 1.

8 papers.

  1. 2021Sparse Spatial Transformers for Few-Shot Learningcited by 14
  2. 2022Learning to Learn: How to Continuously Teach Humans and Machinescited by 9
  3. 2022ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learningcited by 6
  4. 2022GDC- Generalized Distribution Calibration for Few-Shot Learningcited by 5
  5. 2022Few-shot Fine-grained Image Classification via Multi-Frequency Neighborhood and Double-cross Modulationcited by 4
  6. 2023Boosting Audio-visual Zero-shot Learning with Large Language Modelscited by 1
  7. 2022Learning Primitive-aware Discriminative Representations for Few-shot Learningcited by 0
  8. 2026Few-Shot Video Recognition via Hierarchical Metric Learningcited by 0