MACL: Multi-Label Adaptive Contrastive Learning Loss for Remote Sensing Image Retrieval

• URL: http://arxiv.org/abs/2512.16294v1
• Date: Thu, 18 Dec 2025 08:29:27 GMT
• Title: MACL: Multi-Label Adaptive Contrastive Learning Loss for Remote Sensing Image Retrieval
• Abstract summary: Multi-Label Adaptive Contrastive Learning (MACL) is introduced as an extension of contrastive learning to address them.<n>It integrates label-aware sampling, frequency-sensitive weighting, and dynamic-temperature scaling to achieve balanced representation learning across both common and rare categories.
• Score: 4.411658619208916
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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/MACL upon acceptance.

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