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.
@article{arxiv.2603.11783,
title = {HELM: Hierarchical and Explicit Label Modeling with Graph Learning for Multi-Label Image Classification},
author = {Marjan Stoimchev and Boshko Koloski and Jurica Levatić and Dragi Kocev and Sašo Džeroski},
journal= {arXiv preprint arXiv:2603.11783},
year = {2026}
}
Comments
Accepted and presented at REO workshop at EurIPS 2025