  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.
