Few-Shot Generalized Category Discovery With Retrieval-Guided Decision Boundary Enhancement

TL;DR

This paper introduces Few-Shot Generalized Category Discovery (FSGCD), a new task focusing on discovering categories with limited labeled data, and proposes a retrieval-guided decision boundary enhancement framework that outperforms existing methods.

Contribution

The paper presents a novel FSGCD task and a decision boundary enhancement framework with affinity-based retrieval to improve category discovery with scarce labeled samples.

Findings

Outperforms existing methods on six GCD benchmarks.

Effectively learns decision boundaries with limited labeled data.

Enhances unknown category recognition through retrieval-guided boundary refinement.

Abstract

While existing Generalized Category Discovery (GCD) models have achieved significant success, their performance with limited labeled samples and a small number of known categories remains largely unexplored. In this work, we introduce the task of Few-shot Generalized Category Discovery (FSGCD), aiming to achieve competitive performance in GCD tasks under conditions of known information scarcity. To tackle this challenge, we propose a decision boundary enhancement framework with affinity-based retrieval. Our framework is designed to learn the decision boundaries of known categories and transfer these boundaries to unknown categories. First, we use a decision boundary pre-training module to mitigate the overfitting of pre-trained information on known category boundaries and improve the learning of these decision boundaries using labeled samples. Second, we implement a two-stage retrieval-guided decision boundary optimization strategy. Specifically, this strategy further enhances the severely limited known boundaries by using affinity-retrieved pseudo-labeled samples. Then, these refined boundaries are applied to unknown clusters via guidance from affinity-based feature retrieval. Experimental results demonstrate that our proposed method outperforms existing methods on six public GCD benchmarks under the FSGCD setting. The codes are available at: https://github.com/Ryh1218/FSGCD