Generalized Fine-Grained Category Discovery with Multi-Granularity Conceptual Experts

• URL: http://arxiv.org/abs/2509.26227v1
• Date: Tue, 30 Sep 2025 13:25:11 GMT
• Title: Generalized Fine-Grained Category Discovery with Multi-Granularity Conceptual Experts
• Authors: Haiyang Zheng, Nan Pu, Wenjing Li, Nicu Sebe, Zhun Zhong,
• Abstract summary: Generalized Category Discovery is an open-world problem that clusters unlabeled data by leveraging knowledge from partially labeled categories.<n>Existing approaches fail to exploit multi-granularity conceptual information in visual data.<n>We propose a Multi-Granularity Experts framework that integrates multi-granularity knowledge for accurate category discovery.
• Score: 81.68203255687051
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generalized Category Discovery (GCD) is an open-world problem that clusters unlabeled data by leveraging knowledge from partially labeled categories. A key challenge is that unlabeled data may contain both known and novel categories. Existing approaches suffer from two main limitations. First, they fail to exploit multi-granularity conceptual information in visual data, which limits representation quality. Second, most assume that the number of unlabeled categories is known during training, which is impractical in real-world scenarios. To address these issues, we propose a Multi-Granularity Conceptual Experts (MGCE) framework that adaptively mines visual concepts and integrates multi-granularity knowledge for accurate category discovery. MGCE consists of two modules: (1) Dynamic Conceptual Contrastive Learning (DCCL), which alternates between concept mining and dual-level representation learning to jointly optimize feature learning and category discovery; and (2) Multi-Granularity Experts Collaborative Learning (MECL), which extends the single-expert paradigm by introducing additional experts at different granularities and by employing a concept alignment matrix for effective cross-expert collaboration. Importantly, MGCE can automatically estimate the number of categories in unlabeled data, making it suitable for practical open-world settings. Extensive experiments on nine fine-grained visual recognition benchmarks demonstrate that MGCE achieves state-of-the-art results, particularly in novel-class accuracy. Notably, even without prior knowledge of category numbers, MGCE outperforms parametric approaches that require knowing the exact number of categories, with an average improvement of 3.6\%. Code is available at https://github.com/HaiyangZheng/MGCE.

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