AeroRAG: Structured Multimodal Retrieval-Augmented LLM for Fine-Grained Aerial Visual Reasoning

TL;DR

AeroRAG introduces a scene-graph-guided multimodal retrieval-augmented framework that enhances aerial visual question answering by explicitly structuring visual knowledge for better reasoning.

Contribution

The paper presents AeroRAG, a novel approach that converts aerial images into structured visual knowledge and uses retrieval-augmented prompts for improved reasoning in large language models.

Findings

Significant performance improvements on AUG and VG-150 datasets.

Largest gains observed in dense aerial scenes and relation-sensitive reasoning.

Framework remains compatible with standard visual reasoning benchmarks like VQAv2.

Abstract

Despite recent progress in multimodal large language models (MLLMs), reliable visual question answering in aerial scenes remains challenging. In such scenes, task-critical evidence is often carried by small objects, explicit quantities, coarse locations, and inter-object relations, whereas conventional dense visual-token representations are not well aligned with these structured semantics. To address this interface mismatch, we propose AeroRAG, a scene-graph-guided multimodal retrieval-augmented generation framework for visual question answering. The framework first converts an input image into structured visual knowledge, including object categories, quantities, spatial locations, and semantic relations, and then retrieves query-relevant semantic chunks to construct compact prompts for a text-based large language model. Rather than relying on direct reasoning over dense visual tokens, our method introduces a more explicit intermediate interface between perception and language reasoning. Experiments on the AUG aerial dataset and the general-domain VG-150 benchmark show consistent improvements over six strong MLLM baselines, with the largest gains observed in dense aerial scenes and relation-sensitive reasoning. We further evaluate the framework on VQAv2 to verify that the proposed interface remains compatible with standard visual reasoning settings. These results suggest that structured retrieval is a practical design direction for deployment-oriented and grounded visual reasoning systems.