Deep Meta-Relation Network for Visual Few-Shot Learning

This paper proposes a novel metric-based deep learning method to solve the few-shot learning problem. It models the relation between images as high dimensional vector, and trains a network module to judge, when given two relational features, which one indicates a stronger connection between the image objects. By training such a network module, we introduce a comparative mechanism into the metric space, i.e., the similarity score of any two images is computed after seeing other images in the same task. Further more, we propose to incorporate a batch classification loss into episodic training to mitigate the hard training problem that occurs when embedding network is going deeper. Experiments demonstrate that the proposed network can achieve promising performance.