  Learning from a few examples is a challenging task for machine learning.
While recent progress has been made for this problem, most of the existing
methods ignore the compositionality in visual concept representation (e.g.
objects are built from parts or composed of semantic attributes), which is key
to the human ability to easily learn from a small number of examples. To
enhance the few-shot learning models with compositionality, in this paper we
present the simple yet powerful Compositional Feature Aggregation (CFA) module
as a weakly-supervised regularization for deep networks. Given the deep feature
maps extracted from the input, our CFA module first disentangles the feature
space into disjoint semantic subspaces that model different attributes, and
then bilinearly aggregates the local features within each of these subspaces.
CFA explicitly regularizes the representation with both semantic and spatial
compositionality to produce discriminative representations for few-shot
recognition tasks. Moreover, our method does not need any supervision for
attributes and object parts during training, thus can be conveniently plugged
into existing models for end-to-end optimization while keeping the model size
and computation cost nearly the same. Extensive experiments on few-shot image
classification and action recognition tasks demonstrate that our method
provides substantial improvements over recent state-of-the-art methods.
