  Few-shot learning aims to correctly recognize query samples from unseen
classes given a limited number of support samples, often by relying on global
embeddings of images. In this paper, we propose to equip the backbone network
with an attention agent, which is trained by reinforcement learning. The policy
gradient algorithm is employed to train the agent towards adaptively localizing
the representative regions on feature maps over time. We further design a
reward function based on the prediction of the held-out data, thus helping the
attention mechanism to generalize better across the unseen classes. The
extensive experiments show, with the help of the reinforced attention, that our
embedding network has the capability to progressively generate a more
discriminative representation in few-shot learning. Moreover, experiments on
the task of image classification also show the effectiveness of the proposed
design.
