通过引入局部部件知识，提升细粒度类别发现的准确性。
Learning Part Knowledge to Facilitate Category Understanding for Fine-Grained Generalized Category Discovery
- 用高斯混合模型自动提取类别相关局部部件。
- 在多个细粒度数据集上达到当前最优性能。
- 适合细粒度分类与未知类别发现任务的研究者。
广义类别发现（GCD）旨在对包含已见和新类别标签的无标签数据进行分类。尽管现有方法在通用数据集上表现良好，但在细粒度场景中仍面临挑战。我们归因于其依赖全局图像特征的对比学习自动捕捉判别性线索，却无法捕捉细粒度类别所依赖的细微局部差异。为此，本文提出引入部件知识解决细粒度GCD问题，面临两大挑战：新类别缺乏标注导致部件特征难以提取；全局对比学习强调整体特征不变性，无意中抑制了判别性局部部件模式。为此，我们提出PartGCD，包括1）自适应部件分解，通过高斯混合模型自动提取类别特定语义部件；2）部件差异正则化，显式分离部件特征以增强细粒度局部区分能力。实验表明，该方法在多个细粒度基准上达到最先进性能，同时在通用数据集上保持竞争力，验证了方法的有效性与鲁棒性。
原文摘要 · Abstract (English)
Generalized Category Discovery (GCD) aims to classify unlabeled data containing both seen and novel categories. Although existing methods perform well on generic datasets, they struggle in fine-grained scenarios. We attribute this difficulty to their reliance on contrastive learning over global image features to automatically capture discriminative cues, which fails to capture the subtle local differences essential for distinguishing fine-grained categories. Therefore, in this paper, we propose incorporating part knowledge to address fine-grained GCD, which introduces two key challenges: the absence of annotations for novel classes complicates the extraction of the part features, and global contrastive learning prioritizes holistic feature invariance, inadvertently suppressing discriminative local part patterns. To address these challenges, we propose PartGCD, including 1) Adaptive Part Decomposition, which automatically extracts class-specific semantic parts via Gaussian Mixture Models, and 2) Part Discrepancy Regularization, enforcing explicit separation between part features to amplify fine-grained local part distinctions. Experiments demonstrate state-of-the-art performance across multiple fine-grained benchmarks while maintaining competitiveness on generic datasets, validating the effectiveness and robustness of our approach.
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