提出新框架提升遥感图像多标签分类，尤其在标签少时表现更好。
HELM: Hierarchical and Explicit Label Modeling with Graph Learning for Multi-Label Image Classification
- 用视觉变换器中的层级专属类别标记捕捉标签间复杂关系。
- 通过图卷积网络显式建模层级结构，生成具备层级感知的特征表示。
- 引入自监督分支，有效利用无标签遥感图像数据，适合小样本场景。
层级多标签分类（HMLC）在遥感图像中对复杂标签依赖关系建模至关重要。现有方法在多路径层级结构下表现不佳，且很少利用无标签数据。本文提出HELM（Hierarchical and Explicit Label Modeling）框架，克服上述问题：(i) 在视觉变换器中使用层级专属类别标记，以捕捉精细的标签交互；(ii) 采用图卷积网络显式编码层级结构，生成具有层级感知的嵌入；(iii) 集成自监督分支，高效利用无标签遥感图像。在四个遥感图像数据集（UCM、AID、DFC-15、MLRSNet）上进行综合评估，HELM在监督与半监督设置下均达到最优性能，尤其在低标签场景中表现突出。
原文摘要 · Abstract (English)
Hierarchical multi-label classification (HMLC) is essential for modeling complex label dependencies in remote sensing. Existing methods, however, struggle with multi-path hierarchies where instances belong to multiple branches, and they rarely exploit unlabeled data. We introduce HELM (\textit{Hierarchical and Explicit Label Modeling}), a novel framework that overcomes these limitations. HELM: (i) uses hierarchy-specific class tokens within a Vision Transformer to capture nuanced label interactions; (ii) employs graph convolutional networks to explicitly encode the hierarchical structure and generate hierarchy-aware embeddings; and (iii) integrates a self-supervised branch to effectively leverage unlabeled imagery. We perform a comprehensive evaluation on four remote sensing image (RSI) datasets (UCM, AID, DFC-15, MLRSNet). HELM achieves state-of-the-art performance, consistently outperforming strong baselines in both supervised and semi-supervised settings, demonstrating particular strength in low-label scenarios.
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