ONIR: Object-Noted Tagging for Aerial Image Captioning generation

Automated captioning for remote sensing imagery often struggles to balance the high descriptive power of large models with the deployment feasibility of smaller ones. To bridge this gap, this paper introduces ONIR, a LLM-efficient, tag-guided framework that empowers compact language models (1-3B parameters) to achieve state-of-the-art captioning accuracy. Specifically, the proposed approach synthesizes a large-scale pseudo-caption dataset by leveraging GPT-4O on existing segmentation benchmarks. Explicit semantic tags are then extracted to train a multi-label Contrastive Language-Image Pre-Training (CLIP) encoder, providing interpretable visual guidance. To maintain parameter efficiency, the architecture incorporates a simple Multilayer Perceptron (MLP) bridge and a two-stage LoRA fine-tuning strategy. Extensive experiments on standard benchmark dataset, such as UCM and Sydney Captions, demonstrate that ONIR significantly outperforms models up to four times its size (7-13B). By combining superior performance with computational efficiency and tag-based controllability, ONIR offers a highly practical solution for real-world remote sensing applications.