# Parameter-Efficient Transfer Learning for Remote Sensing Image-Text Retrieval

arXiv: https://arxiv.org/abs/2308.12509 · HTML: https://mlsanity.com/paper/2308.12509/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 51 of its 77 references that are in the map.
- First posted on arXiv 24 Aug 2023.

## Builds on (most cited first, 30 of 51)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [Conditional Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2203.05557/) (2022)
- [Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks](https://mlsanity.com/paper/2004.06165/) (2020)
- [CLIP-Adapter: Better Vision-Language Models with Feature Adapters](https://mlsanity.com/paper/2110.04544/) (2021)
- [AdapterFusion: Non-Destructive Task Composition for Transfer Learning](https://mlsanity.com/paper/2005.00247/) (2020)
- [AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition](https://mlsanity.com/paper/2205.13535/) (2022)
- [MaPLe: Multi-modal Prompt Learning](https://mlsanity.com/paper/2210.03117/) (2022)
- [SimVLM: Simple Visual Language Model Pretraining with Weak Supervision](https://mlsanity.com/paper/2108.10904/) (2021)
- [Compacter: Efficient Low-Rank Hypercomplex Adapter Layers](https://mlsanity.com/paper/2106.04647/) (2021)
- [Stacked Cross Attention for Image-Text Matching](https://mlsanity.com/paper/1803.08024/) (2018)
- [Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classification](https://mlsanity.com/paper/2207.09519/) (2022)
- [VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks](https://mlsanity.com/paper/2112.06825/) (2021)
- [Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks](https://mlsanity.com/paper/2106.04489/) (2021)
- [AdapterDrop: On the Efficiency of Adapters in Transformers](https://mlsanity.com/paper/2010.11918/) (2020)
- [RSVQA: Visual Question Answering for Remote Sensing Data](https://mlsanity.com/paper/2003.07333/) (2020)
- [Exploring Models and Data for Remote Sensing Image Caption Generation](https://mlsanity.com/paper/1712.07835/) (2017)
- [ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning](https://mlsanity.com/paper/2206.13559/) (2022)
- [CPT: Colorful Prompt Tuning for Pre-trained Vision-Language Models](https://mlsanity.com/paper/2109.11797/) (2021)
- [A Survey of Vision-Language Pre-Trained Models](https://mlsanity.com/paper/2202.10936/) (2022)
- [VLP: A Survey on Vision-Language Pre-training](https://mlsanity.com/paper/2202.09061/) (2022)
- [Unified Vision and Language Prompt Learning](https://mlsanity.com/paper/2210.07225/) (2022)

## Built on by (most cited first, 24 of 24)

- [SkyEyeGPT: Unifying Remote Sensing Vision-Language Tasks via Instruction Tuning with Large Language Model](https://mlsanity.com/paper/2401.09712/) (2024)
- [Prompt-based Adaptation in Large-scale Vision Models: A Survey](https://mlsanity.com/paper/2510.13219/) (2025)
- [A Survey on Remote Sensing Foundation Models: From Vision to Multimodality](https://mlsanity.com/paper/2503.22081/) (2025)
- [M2IST: Multi-Modal Interactive Side-Tuning for Efficient Referring Expression Comprehension](https://mlsanity.com/paper/2407.01131/) (2024)
- [Parameter-Efficient Adaptation of Geospatial Foundation Models through Embedding Deflection](https://mlsanity.com/paper/2503.09493/) (2025)
- [Knowledge-aware Text-Image Retrieval for Remote Sensing Images](https://mlsanity.com/paper/2405.03373/) (2024)
- [SwimVG: Step-wise Multimodal Fusion and Adaption for Visual Grounding](https://mlsanity.com/paper/2502.16786/) (2025)
- [WeatherPrompt: Multi-modality Representation Learning for All-Weather Drone Visual Geo-Localization](https://mlsanity.com/paper/2508.09560/) (2025)
- [DARA: Domain- and Relation-aware Adapters Make Parameter-efficient Tuning for Visual Grounding](https://mlsanity.com/paper/2405.06217/) (2024)
- [PriorCLIP: Visual Prior Guided Vision-Language Model for Remote Sensing Image-Text Retrieval](https://mlsanity.com/paper/2405.10160/) (2024)
- [Transcending Fusion: A Multi-Scale Alignment Method for Remote Sensing Image-Text Retrieval](https://mlsanity.com/paper/2405.18959/) (2024)
- [Empirical Study of PEFT techniques for Winter Wheat Segmentation](https://mlsanity.com/paper/2310.01825/) (2023)
- [Self-Supervised Cross-Modal Text-Image Time Series Retrieval in Remote Sensing](https://mlsanity.com/paper/2501.19043/) (2025)
- [Survey of Multimodal Geospatial Foundation Models: Techniques, Applications, and Challenges](https://mlsanity.com/paper/2510.22964/) (2025)
- [Efficient Remote Sensing with Harmonized Transfer Learning and Modality Alignment](https://mlsanity.com/paper/2404.18253/) (2024)
- [Cross-Modal Pre-Aligned Method with Global and Local Information for Remote-Sensing Image and Text Retrieval](https://mlsanity.com/paper/2411.14704/) (2024)
- [Optimal Transport Adapter Tuning for Bridging Modality Gaps in Few-Shot Remote Sensing Scene Classification](https://mlsanity.com/paper/2503.14938/) (2025)
- [iEBAKER: Improved Remote Sensing Image-Text Retrieval Framework via Eliminate Before Align and Keyword Explicit Reasoning](https://mlsanity.com/paper/2504.05644/) (2025)
- [Representation Discrepancy Bridging Method for Remote Sensing Image-Text Retrieval](https://mlsanity.com/paper/2505.16756/) (2025)
- [TCMA: Text-Conditioned Multi-granularity Alignment for Drone Cross-Modal Text-Video Retrieval](https://mlsanity.com/paper/2510.10180/) (2025)
- [An Efficient and Effective Encoder Model for Vision and Language Tasks in the Remote Sensing Domain](https://mlsanity.com/paper/2512.15531/) (2025)
- [Multi-Perspective Subimage CLIP with Keyword Guidance for Remote Sensing Image-Text Retrieval](https://mlsanity.com/paper/2601.18190/) (2026)
- [Robust Remote Sensing Image-Text Retrieval with Noisy Correspondence](https://mlsanity.com/paper/2603.28134/) (2026)
- [AlignJEPA: Predictive Vision-Language Alignment for Remote Sensing Foundation Models](https://mlsanity.com/paper/2608.15456/) (2026)

## Abstract (arXiv)

Vision-and-language pre-training (VLP) models have experienced a surge in popularity recently. By fine-tuning them on specific datasets, significant performance improvements have been observed in various tasks. However, full fine-tuning of VLP models not only consumes a significant amount of computational resources but also has a significant environmental impact. Moreover, as remote sensing (RS) data is constantly being updated, full fine-tuning may not be practical for real-world applications. To address this issue, in this work, we investigate the parameter-efficient transfer learning (PETL) method to effectively and efficiently transfer visual-language knowledge from the natural domain to the RS domain on the image-text retrieval task. To this end, we make the following contributions. 1) We construct a novel and sophisticated PETL framework for the RS image-text retrieval (RSITR) task, which includes the pretrained CLIP model, a multimodal remote sensing adapter, and a hybrid multi-modal contrastive (HMMC) learning objective; 2) To deal with the problem of high intra-modal similarity in RS data, we design a simple yet effective HMMC loss; 3) We provide comprehensive empirical studies for PETL-based RS image-text retrieval. Our results demonstrate that the proposed method is promising and of great potential for practical applications. 4) We benchmark extensive state-of-the-art PETL methods on the RSITR task. Our proposed model only contains 0.16M training parameters, which can achieve a parameter reduction of 98.9% compared to full fine-tuning, resulting in substantial savings in training costs. Our retrieval performance exceeds traditional methods by 7-13% and achieves comparable or better performance than full fine-tuning. This work can provide new ideas and useful insights for RS vision-language tasks.