Generalized Category Discovery under the Long-Tailed Distribution

Bingchen Zhao; Kai Han

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arXiv · 2506.12515

Generalized Category Discovery under the Long-Tailed Distribution

Bingchen Zhao·Kai Han

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Abstract

This paper addresses the problem of Generalized Category Discovery (GCD) under a long-tailed distribution, which involves discovering novel categories in an unlabelled dataset using knowledge from a set of labelled categories. Existing works assume a uniform distribution for both datasets, but real-world data often exhibits a long-tailed distribution, where a few categories contain most examples, while others have only a few. While the long-tailed distribution is well-studied in supervised and semi-supervised settings, it remains unexplored in the GCD context. We identify two challenges in this setting - balancing classifier learning and estimating category numbers - and propose a framework based on confident sample selection and density-based clustering to tackle them. Our experiments on both long-tailed and conventional GCD datasets demonstrate the effectiveness of our method.

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BibTeX RIS

Bingchen Zhao, Kai Han. 2025-06-14. Generalized Category Discovery under the Long-Tailed Distribution. https://arxiv.org/abs/2506.12515

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