Parameter-Efficient Remote Sensing Image-Text Retrieval via Hierarchical Gated Multi-Modal Adapters

This paper proposes a parameter-efficient fine-tuning framework
based on a Hierarchical Gated Multi-modal Adapter (HGMA) for remote sensing
image-text retrieval. To address the limitations of traditional models and the high
computational costs of full-parameter fine-tuning, this method introduces a
multi-head self-attention module and a hierarchical gating mechanism to dynamically regulate cross-modal features across different depths. Furthermore, a
Cross-modal Semantic Perturbation (CMSP) data augmentation strategy is designed to generate semantically consistent pseudo-samples, effectively enhancing the model's resistance to noise interference and domain shift. Extensive experiments on RSICD and RSITMD benchmark datasets demonstrate that the proposed method achieves performance comparable to full-parameter fine-tuning
while training only about 0.6M parameters.