Reweighting and information-guidance networks for Few-Shot Learning

Few-Shot Learning (FSL) aims at recognizing new categories from a few available samples. In this paper, we propose two strategies on the basis of Prototypical Networks [1] to improve the discriminativeness and representativeness of the visual prototypes for few-shot learning task. Firstly, we propose a reweighting mechanism to distribute different weights for accesses the instance representativeness to the class. Secondly, we propose an information-guidance mechanism to encode discriminative knowledge into the class prototypes to compensate for more information across classes. Extensive experimental results on two benchmark datasets empirically show that both the proposed strategies improve the Prototypical Networks and achieve the state-of-the-art performances. Besides, the information-guidance mechanism could be seamlessly combined into some existing approaches to substantially improve their performances on few-shot classification.