通过建模层级间关系提升分类性能，显著优于现有方法。
Feature Identification for Hierarchical Contrastive Learning
- 用高斯混合与注意力机制捕捉层级特征，模拟人类认知
- 在CIFAR100和ModelNet40上线性评估准确率领先2个百分点
- 适合需要细粒度分类的计算机视觉任务
层级分类在众多应用中至关重要，物体按多级类别组织。但传统分类方法常忽略不同层级间的类间关系，导致丢失重要监督信号。为此，我们提出两种新型层级对比学习方法：基于高斯混合模型的G-HMLC与利用注意力机制的A-HMLC，模仿人类处理方式。我们的方法显式建模高层级的类间关系与类别分布不均衡问题，实现全层级的细粒度聚类。在竞争性的CIFAR100与ModelNet40数据集上，该方法在线性评估中达到当前最优性能，相比现有层级对比学习方法准确率提升2个百分点。定量与定性结果均验证了方法的有效性，表明其在计算机视觉等领域的应用潜力。
原文摘要 · Abstract (English)
Hierarchical classification is a crucial task in many applications, where objects are organized into multiple levels of categories. However, conventional classification approaches often neglect inherent inter-class relationships at different hierarchy levels, thus missing important supervisory signals. Thus, we propose two novel hierarchical contrastive learning (HMLC) methods. The first, leverages a Gaussian Mixture Model (G-HMLC) and the second uses an attention mechanism to capture hierarchy-specific features (A-HMLC), imitating human processing. Our approach explicitly models inter-class relationships and imbalanced class distribution at higher hierarchy levels, enabling fine-grained clustering across all hierarchy levels. On the competitive CIFAR100 and ModelNet40 datasets, our method achieves state-of-the-art performance in linear evaluation, outperforming existing hierarchical contrastive learning methods by 2 percentage points in terms of accuracy. The effectiveness of our approach is backed by both quantitative and qualitative results, highlighting its potential for applications in computer vision and beyond.
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