HELM: Hierarchical and Explicit Label Modeling with Graph Learning for Multi-Label Image Classification

• URL: http://arxiv.org/abs/2603.11783v1
• Date: Thu, 12 Mar 2026 10:41:17 GMT
• Title: HELM: Hierarchical and Explicit Label Modeling with Graph Learning for Multi-Label Image Classification
• Abstract summary: Hierarchical multi-label classification (HMLC) is essential for modeling complex label dependencies in remote sensing.<n>We introduce HELM (textitHierarchical and Explicit Label Modeling), a novel framework that overcomes these limitations.<n>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.
• Score: 2.165423459607678
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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