Focused Adapter: Enhancing Fine-Grained Attention for Remote Sensing Image–Text Retrieval

Abstract

The emergence of large-scale vision–language models (VLMs) has significantly advanced remote sensing image–text retrieval (RSITR) by providing powerful cross-modal semantic priors. However, when adapted to the remote sensing (RS) domain, these models struggle to capture fine-grained representations due to their inherent global semantic bias. To address these limitations, we propose the focused adapter (FocA), a plug-and-play parameter-efficient fine-tuning (PEFT) architecture designed to enhance fine-grained perception from frozen VLMs. The FocA features a hybrid structure consisting of two components: an enhancement adapter and an alignment adapter. The enhancement adapter utilizes bottleneck self-attention to capture rich patch-level details often lost during global pooling. The alignment adapter incorporates a cross-modal shared projection subspace to facilitate early feature interaction and implicit alignment. In addition, we develop an explicit shared loss to provide direct semantic supervision, preventing the dilution of critical features during deep propagation. Extensive experiments on the RSICD and RSITMD datasets demonstrate that FocA achieves state-of-the-art (SOTA) performance, attaining a mean recall (mR) of 37.61% on RSICD and 48.95% on RSITMD. Our code is available at https://github.com/WenliangDu/FocA