  Few-shot image classification consists of two consecutive learning processes:
1) In the meta-learning stage, the model acquires a knowledge base from a set
of training classes. 2) During meta-testing, the acquired knowledge is used to
recognize unseen classes from very few examples. Inspired by the compositional
representation of objects in humans, we train a neural network architecture
that explicitly represents objects as a dictionary of shared components and
their spatial composition. In particular, during meta-learning, we train a
knowledge base that consists of a dictionary of component representations and a
dictionary of component activation maps that encode common spatial activation
patterns of components. The elements of both dictionaries are shared among the
training classes. During meta-testing, the representation of unseen classes is
learned using the component representations and the component activation maps
from the knowledge base. Finally, an attention mechanism is used to strengthen
those components that are most important for each category. We demonstrate the
value of our interpretable compositional learning framework for a few-shot
classification using miniImageNet, tieredImageNet, CIFAR-FS, and FC100, where
we achieve comparable performance.
