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

arXiv: https://arxiv.org/abs/2512.16294 · HTML: https://mlsanity.com/paper/2512.16294/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 41 references that are in the map.
- First posted on arXiv 18 Dec 2025.

## Builds on (most cited first, 12 of 12)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Relation Network for Multi-label Aerial Image Classification](https://mlsanity.com/paper/1907.07274/) (2019)
- [Enhancing Remote Sensing Image Retrieval with Triplet Deep Metric Learning Network](https://mlsanity.com/paper/1902.05818/) (2019)
- [Informative and Representative Triplet Selection for Multilabel Remote Sensing Image Retrieval](https://mlsanity.com/paper/2105.03647/) (2021)
- [A Novel Graph-Theoretic Deep Representation Learning Method for Multi-Label Remote Sensing Image Retrieval](https://mlsanity.com/paper/2106.00506/) (2021)
- [Exploring Contrastive Learning for Long-Tailed Multi-Label Text Classification](https://mlsanity.com/paper/2404.08720/) (2024)
- [Class Prototypes based Contrastive Learning for Classifying Multi-Label and Fine-Grained Educational Videos](https://mlsanity.com/paper/2510.11204/) (2025)
- [Multi-Label Contrastive Learning : A Comprehensive Study](https://mlsanity.com/paper/2412.00101/) (2024)
- [Asymmetric Hash Code Learning for Remote Sensing Image Retrieval](https://mlsanity.com/paper/2201.05772/) (2022)
- [Similarity-Dissimilarity Loss for Multi-label Supervised Contrastive Learning](https://mlsanity.com/paper/2410.13439/) (2024)
- [Region Convolutional Features for Multi-Label Remote Sensing Image Retrieval](https://mlsanity.com/paper/1807.08634/) (2018)

## Abstract (arXiv)

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.