Few-shot open-set recognition via pairwise discriminant aggregation

Abstract

Few-shot open-set recognition (FSOR) aims to develop models capable of generalizing to new tasks for both unknown detection and known classification with limited labeled data. Previous FSOR methods lack a comprehensive definition of the open space in the few-shot scenario, making models susceptible to overgeneralization. In this paper, we propose a novel method called Pairwise Discriminant Aggregation (PDAgg), addressing FSOR within a two-level recognition framework. PDAgg unifies the diverse optimization goals of FSOR at the pair level and provides a reasonable aggregate-level representation for unknown samples, thereby greatly enhancing model generalization to open space in the few-shot context. Specifically, PDAgg treats support-query pairs as the basic recognition units, which are adapted to a pair-specific feature space by enhancing pairwise representative features and incorporating a global knowledge context. Binary discriminant analyses are performed on adapted pair embeddings to estimate pair-level discriminant scores, which are then jointly aggregated to achieve both unknown detection and few-shot classification. Extensive experiments demonstrate that our method delivers comparable and even better performance with less extra information compared to the existing FSOR methods.