mlsanityData to 13 Oct 2026

Few-shot Image Classification with Multi-Facet Prototypes

arXiv 2102.00801 ↗First posted 1 Feb 2021cs.CV cs.AIcited by 3

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Abstract (arXiv)

The aim of few-shot learning (FSL) is to learn how to recognize image categories from a small number of training examples. A central challenge is that the available training examples are normally insufficient to determine which visual features are most characteristic of the considered categories. To address this challenge, we organize these visual features into facets, which intuitively group features of the same kind (e.g. features that are relevant to shape, color, or texture). This is motivated from the assumption that (i) the importance of each facet differs from category to category and (ii) it is possible to predict facet importance from a pre-trained embedding of the category names. In particular, we propose an adaptive similarity measure, relying on predicted facet importance weights for a given set of categories. This measure can be used in combination with a wide array of existing metric-based methods. Experiments on miniImageNet and CUB show that our approach improves the state-of-the-art in metric-based FSL.

Key facts. Cited by 3 papers in the map (data through 13 Oct 2026). Builds on 18 of its 27 references that are in the map. First posted on arXiv 1 Feb 2021.

Builds on

18 of 27 references.

References · 18

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6 more
  1. 2017Meta-SGD: Learning to Learn Quickly for Few-Shot Learningcited by 378
  2. 2018Meta-Transfer Learning for Few-Shot Learningcited by 306
  3. 2018Few-Shot Learning via Embedding Adaptation with Set-to-Set Functionscited by 294
  4. 2019Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learningcited by 121
  5. 2019Adaptive Cross-Modal Few-Shot Learningcited by 95
  6. 2020Boosting Few-Shot Learning With Adaptive Margin Losscited by 83

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Cited by year

Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 1.

3 papers.

  1. 2021Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attentioncited by 15
  2. 2021Enhancing Prototypical Few-Shot Learning by Leveraging the Local-Level Strategycited by 2
  3. 2024Modeling Multi-modal Cross-interaction for Multi-label Few-shot Image Classification Based on Local Feature Selectioncited by 0