MACL: Multi-Label Adaptive Contrastive Learning Loss for Remote Sensing Image Retrieval
It addresses retrieval problems for remote-sensing archives, offering incremental improvements over existing contrastive learning methods.
The paper tackles challenges in multi-label remote-sensing image retrieval, such as semantic overlap and imbalanced labels, by introducing MACL, which outperforms baselines on three benchmark datasets.
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.