通过部件级对应关系提升类别发现准确率
PartCo: Part-Level Correspondence Priors Enhance Category Discovery
- 引入部件级视觉对应先验，捕捉细粒度语义结构
- 在多个基准数据集上显著提升现有方法性能
- 可无缝集成到已有框架，适合图像分类任务
通用类别发现（GCD）旨在利用已知类别的标注样本，在未标注数据中识别已知和新类别。现有方法主要依赖语义标签和全局图像表示，常忽略对区分密切相关的类别至关重要的部件级线索。本文提出PartCo（部件级对应先验），一种通过引入部件级视觉特征对应关系来增强类别发现的新框架。借助部件间关系，PartCo能够捕捉更精细的语义结构，实现对类别关系的更深入理解。重要的是，PartCo可无缝集成至现有GCD方法，无需重大修改。大量实验表明，该方法显著提升了当前GCD方法的性能，在多个基准数据集上超越多数现有方法，弥合了语义标签与部件级视觉构型之间的差距，为GCD设立了新基准。
原文摘要 · Abstract (English)
Generalized Category Discovery (GCD) aims to identify both known and novel categories within unlabeled data by leveraging a set of labeled examples from known categories. Existing GCD methods primarily depend on semantic labels and global image representations, often overlooking the detailed part-level cues that are crucial for distinguishing closely related categories. In this paper, we introduce PartCo, short for Part-Level Correspondence Prior, a novel framework that enhances category discovery by incorporating part-level visual feature correspondences. By leveraging part-level relationships, PartCo captures finer-grained semantic structures, enabling a more nuanced understanding of category relationships. Importantly, PartCo seamlessly integrates with existing GCD methods without requiring significant modifications. Our extensive experiments on multiple benchmark datasets demonstrate that PartCo significantly improves the performance of current GCD approaches, outperforming most existing methods by bridging the gap between semantic labels and part-level visual compositions, thereby setting new benchmarks for GCD.
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