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arXiv · 2512.16294

MARC: Multi-Label Adaptive Retrieval Contrastive Loss for Remote Sensing Images

Amna Amir·Erchan Aptoula
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Abstract

Semantic overlap among land-cover categories, highly imbalanced label distributions, and complex inter-class co-occurrence patterns constitute significant challenges for multi-label remote-sensing image retrieval. In this article, Multi-Label Adaptive Contrastive Learning (MACL) is introduced as an extension of contrastive learning to address them. It integrates label-aware sampling, frequency-sensitive weighting, and dynamic-temperature scaling to achieve balanced representation learning across both common and rare categories. Extensive experiments on three benchmark datasets (DLRSD, ML-AID, and WHDLD), show that MACL consistently outperforms contrastive-loss based baselines, effectively mitigating semantic imbalance and delivering more reliable retrieval performance in large-scale remote-sensing archives. Code, pretrained models, and evaluation scripts will be released at https://github.com/Amna-128/MARC upon acceptance.

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Amna Amir, Erchan Aptoula. 2025-12-18. MARC: Multi-Label Adaptive Retrieval Contrastive Loss for Remote Sensing Images. https://arxiv.org/abs/2512.16294

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