SEAL: Semantic-Aware Hierarchical Learning for Generalized Category Discovery

• URL: http://arxiv.org/abs/2510.18740v1
• Date: Tue, 21 Oct 2025 15:44:47 GMT
• Title: SEAL: Semantic-Aware Hierarchical Learning for Generalized Category Discovery
• Authors: Zhenqi He, Yuanpei Liu, Kai Han,
• Abstract summary: This paper investigates the problem of Generalized Category Discovery (GCD)<n>Given a partially labelled dataset, GCD aims to categorize all unlabelled images, regardless of whether they belong to known or unknown classes.<n>We introduce a SEmantic-aware hierArchical Learning framework (SEAL), guided by naturally occurring and easily accessible hierarchical structures.
• Score: 17.624912732260672
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper investigates the problem of Generalized Category Discovery (GCD). Given a partially labelled dataset, GCD aims to categorize all unlabelled images, regardless of whether they belong to known or unknown classes. Existing approaches typically depend on either single-level semantics or manually designed abstract hierarchies, which limit their generalizability and scalability. To address these limitations, we introduce a SEmantic-aware hierArchical Learning framework (SEAL), guided by naturally occurring and easily accessible hierarchical structures. Within SEAL, we propose a Hierarchical Semantic-Guided Soft Contrastive Learning approach that exploits hierarchical similarity to generate informative soft negatives, addressing the limitations of conventional contrastive losses that treat all negatives equally. Furthermore, a Cross-Granularity Consistency (CGC) module is designed to align the predictions from different levels of granularity. SEAL consistently achieves state-of-the-art performance on fine-grained benchmarks, including the SSB benchmark, Oxford-Pet, and the Herbarium19 dataset, and further demonstrates generalization on coarse-grained datasets. Project page: https://visual-ai.github.io/seal/

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