Learning Class-level Prototypes for Few-shot Learning
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Abstract (arXiv)
Few-shot learning aims to recognize new categories using very few labeled samples. Although few-shot learning has witnessed promising development in recent years, most existing methods adopt an average operation to calculate prototypes, thus limited by the outlier samples. In this work, we propose a simple yet effective framework for few-shot classification, which can learn to generate preferable prototypes from few support data, with the help of an episodic prototype generator module. The generated prototype is meant to be close to a certain textittargetproto and is less influenced by outlier samples. Extensive experiments demonstrate the effectiveness of this module, and our approach gets a significant raise over baseline models, and get a competitive result compared to previous methods on textitminiImageNet, textittieredImageNet, and cross-domain (textitminiImageNet rightarrow CUB-200-2011) datasets.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 24 of its 38 references that are in the map. First posted on arXiv 25 Aug 2021.
Builds on
24 of 38 references.
References · 24
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12 more
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- 2019Boosting Few-Shot Visual Learning with Self-Supervisioncited by 140
- 2020Adversarial Feature Hallucination Networks for Few-Shot Learningcited by 110
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Citing papers per year: 2026: 1.
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