Overall positive prototype for few-shot open-set recognition

Abstract

Few-shot open-set recognition (FSOR) is the task of recognizing samples in known classes with a limited number of annotated instances while also detecting samples that do not belong to any known class. This is a challenging problem because the models must learn to generalize from a small number of labeled samples and distinguish them from an unlimited number of potential negative examples. In this paper, we propose a novel approach called overall positive prototype to effectively improve performance. Conceptually, negative samples would distribute throughout the feature space and are hard to be described. From the opposite viewpoint, we propose to construct an overall positive prototype that acts as a cohesive representation for positive samples that distribute in a relatively smaller neighborhood. By measuring the distance between a query sample and the overall positive prototype, we can effectively classify it as either positive or negative. We show that this simple yet innovative approach provides the state-of-the-art FSOR performance in terms of accuracy and AUROC.