少样本下提升未知类别发现能力，通过检索增强决策边界。
Few-Shot Generalized Category Discovery With Retrieval-Guided Decision Boundary Enhancement
- 用亲和力检索获取伪标签，优化已知类别的决策边界。
- 在六大数据集上显著优于现有方法，少样本场景下准确率提升超10%。
- 适合小样本、未知类别多的开放世界识别任务。
现有广义类别发现（GCD）模型虽取得显著进展，但在标注样本极少、已知类别数量有限的情况下性能仍不明确。本文提出少样本广义类别发现（FSGCD）任务，旨在信息稀缺条件下实现有竞争力的GCD表现。为此，我们设计一种基于亲和力检索的决策边界增强框架：首先通过决策边界预训练模块缓解预训练特征对已知类别边界的过拟合，提升边界学习能力；其次采用两阶段检索引导的边界优化策略，利用亲和力检索获得的伪标签样本进一步增强有限的已知边界，再通过亲和力特征检索引导将优化后的边界迁移至未知簇。实验表明，在六个公开的GCD基准上，该方法在FSGCD设置下均优于现有方法。代码已开源。
原文摘要 · Abstract (English)
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
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