mlsanityData to 14 Oct 2026

Adaptive Parametric Prototype Learning for Cross-Domain Few-Shot Classification

arXiv 2309.01342 ↗First posted 4 Sep 2023cs.CV cs.LGcited by 4

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Abstract (arXiv)

Cross-domain few-shot classification induces a much more challenging problem than its in-domain counterpart due to the existence of domain shifts between the training and test tasks. In this paper, we develop a novel Adaptive Parametric Prototype Learning (APPL) method under the meta-learning convention for cross-domain few-shot classification. Different from existing prototypical few-shot methods that use the averages of support instances to calculate the class prototypes, we propose to learn class prototypes from the concatenated features of the support set in a parametric fashion and meta-learn the model by enforcing prototype-based regularization on the query set. In addition, we fine-tune the model in the target domain in a transductive manner using a weighted-moving-average self-training approach on the query instances. We conduct experiments on multiple cross-domain few-shot benchmark datasets. The empirical results demonstrate that APPL yields superior performance than many state-of-the-art cross-domain few-shot learning methods.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 30 of its 56 references that are in the map. First posted on arXiv 4 Sep 2023.

Builds on

30 of 56 references.

References · 30

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18 more
  1. 2019Fast AutoAugmentcited by 243
  2. 2016Using Deep Learning for Image-Based Plant Disease Detectioncited by 219
  3. 2018Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learningcited by 211
  4. 2020Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformationcited by 185
  5. 2020CrossTransformers: spatially-aware few-shot transfercited by 174
  6. 2018SpotTune: Transfer Learning through Adaptive Fine-tuningcited by 168
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  9. 2021Cross-Domain Few-Shot Classification via Adversarial Task Augmentationcited by 63
  10. 2020Explanation-Guided Training for Cross-Domain Few-Shot Classificationcited by 58
  11. 2021Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Datacited by 50
  12. 2017Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-tuningcited by 48
  13. 2020Enhancing Few-Shot Image Classification with Unlabelled Examplescited by 32
  14. 2020Cross-Domain Few-Shot Learning by Representation Fusioncited by 32
  15. 2017Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributionscited by 24
  16. 2021Hierarchical Variational Memory for Few-shot Learning Across Domainscited by 14
  17. 2021Cross-domain few-shot learning with unlabelled datacited by 10
  18. 2020Large Margin Mechanism and Pseudo Query Set on Cross-Domain Few-Shot Learningcited by 8

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 2.

4 papers.

  1. 2025Cross-Domain Few-Shot Learning with Coalescent Projections and Latent Space Reservationcited by 2
  2. 2024Hyperspectral Imaging-Based Grain Quality Assessment With Limited Labelled Datacited by 0
  3. 2026FALCON: Few-Shot Adversarial Learning for Cross-Domain Medical Image Segmentationcited by 0
  4. 2026Query-Conditioned Prototype Adaptation for Cross-Domain Few-Shot Learning: Single-Query Inference, Controlled Comparisons, and Failure Modescited by 0