GRASP: Guided Region-Aware Sparse Prompting for Adapting MLLMs to Remote Sensing

• URL: http://arxiv.org/abs/2601.17089v1
• Date: Fri, 23 Jan 2026 10:12:59 GMT
• Title: GRASP: Guided Region-Aware Sparse Prompting for Adapting MLLMs to Remote Sensing
• Authors: Qigan Sun, Chaoning Zhang, Jianwei Zhang, Xudong Wang, Jiehui Xie, Pengcheng Zheng, Haoyu Wang, Sungyoung Lee, Chi-lok Andy Tai, Yang Yang, Heng Tao Shen,
• Abstract summary: We propose a parameter-efficient fine-tuning (PEFT) strategy called Guided Region-Aware Sparse Prompting (GRASP)<n>GRASP introduces spatially structured soft prompts associated with spatial blocks extracted from a frozen visual token grid.<n>Experiments on multiple RSVQA benchmarks show that GRASP achieves competitive performance compared to existing fine-tuning and prompt-based methods.
• Score: 50.961694646995376
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, Multimodal Large Language Models (MLLMs) have made significant progress in visual question answering tasks. However, directly applying existing fine-tuning methods to remote sensing (RS) images often leads to issues such as overfitting on background noise or neglecting target details. This is primarily due to the large-scale variations, sparse target distributions, and complex regional semantic features inherent in RS images. These challenges limit the effectiveness of MLLMs in RS tasks. To address these challenges, we propose a parameter-efficient fine-tuning (PEFT) strategy called Guided Region-Aware Sparse Prompting (GRASP). GRASP introduces spatially structured soft prompts associated with spatial blocks extracted from a frozen visual token grid. Through a question-guided sparse fusion mechanism, GRASP dynamically aggregates task-specific context into a compact global prompt, enabling the model to focus on relevant regions while filtering out background noise. Extensive experiments on multiple RSVQA benchmarks show that GRASP achieves competitive performance compared to existing fine-tuning and prompt-based methods while maintaining high parameter efficiency.

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