# Unsupervised Contrastive Hashing for Cross-Modal Retrieval in Remote Sensing

arXiv: https://arxiv.org/abs/2204.08707 · HTML: https://mlsanity.com/paper/2204.08707/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 23 references that are in the map.
- First posted on arXiv 19 Apr 2022.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [Cross-Modal Contrastive Learning for Text-to-Image Generation](https://mlsanity.com/paper/2101.04702/) (2021)
- [Exploring Models and Data for Remote Sensing Image Caption Generation](https://mlsanity.com/paper/1712.07835/) (2017)
- [CrossCLR: Cross-modal Contrastive Learning For Multi-modal Video Representations](https://mlsanity.com/paper/2109.14910/) (2021)
- [Deep Cross-Modal Hashing](https://mlsanity.com/paper/1602.02255/) (2016)
- [Metric-Learning based Deep Hashing Network for Content Based Retrieval of Remote Sensing Images](https://mlsanity.com/paper/1904.01258/) (2019)
- [Enhancing Remote Sensing Image Retrieval with Triplet Deep Metric Learning Network](https://mlsanity.com/paper/1902.05818/) (2019)
- [Deep Learning for Image Search and Retrieval in Large Remote Sensing Archives](https://mlsanity.com/paper/2004.01613/) (2020)
- [Semantics-Consistent Representation Learning for Remote Sensing Image-Voice Retrieval](https://mlsanity.com/paper/2103.05302/) (2021)

## Built on by (most cited first, 4 of 4)

- [Learning to Evaluate Performance of Multi-modal Semantic Localization](https://mlsanity.com/paper/2209.06515/) (2022)
- [Self-Supervised Cross-Modal Text-Image Time Series Retrieval in Remote Sensing](https://mlsanity.com/paper/2501.19043/) (2025)
- [Redundancy-Aware Pretraining of Vision-Language Foundation Models in Remote Sensing](https://mlsanity.com/paper/2505.11121/) (2025)
- [DEMO: A Statistical Perspective for Efficient Image-Text Matching](https://mlsanity.com/paper/2405.11496/) (2024)

## Abstract (arXiv)

The development of cross-modal retrieval systems that can search and retrieve semantically relevant data across different modalities based on a query in any modality has attracted great attention in remote sensing (RS). In this paper, we focus our attention on cross-modal text-image retrieval, where queries from one modality (e.g., text) can be matched to archive entries from another (e.g., image). Most of the existing cross-modal text-image retrieval systems in RS require a high number of labeled training samples and also do not allow fast and memory-efficient retrieval. These issues limit the applicability of the existing cross-modal retrieval systems for large-scale applications in RS. To address this problem, in this paper we introduce a novel unsupervised cross-modal contrastive hashing (DUCH) method for text-image retrieval in RS. To this end, the proposed DUCH is made up of two main modules: 1) feature extraction module, which extracts deep representations of two modalities; 2) hashing module that learns to generate cross-modal binary hash codes from the extracted representations. We introduce a novel multi-objective loss function including: i) contrastive objectives that enable similarity preservation in intra- and inter-modal similarities; ii) an adversarial objective that is enforced across two modalities for cross-modal representation consistency; and iii) binarization objectives for generating hash codes. Experimental results show that the proposed DUCH outperforms state-of-the-art methods. Our code is publicly available at https://git.tu-berlin.de/rsim/duch.