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Few-shot learning aims to correctly recognize query samples from unseenclasses given a limited number of support samples, often by relying on globalembeddings of images. In this paper, we propose to equip the backbone networkwith an attention agent, which is trained by reinforcement learning. The policygradient algorithm is employed to train the agent towards adaptively localizingthe representative regions on feature maps over time. We further design areward function based on the prediction of the held-out data, thus helping theattention mechanism to generalize better across the unseen classes. Theextensive experiments show, with the help of the reinforced attention, that ourembedding network has the capability to progressively generate a morediscriminative representation in few-shot learning. Moreover, experiments onthe task of image classification also show the effectiveness of the proposeddesign.

Reinforced Attention for Few-Shot Learning and Beyond