Visual question answering (VQA) methods in remote sensing (RS) aim to answer
natural language questions with respect to an RS image. Most of the existing
methods require a large amount of computational resources, which limits their
application in operational scenarios in RS. To address this issue, in this
paper we present an effective lightweight transformer-based VQA in RS
(LiT-4-RSVQA) architecture for efficient and accurate VQA in RS. Our
architecture consists of: i) a lightweight text encoder module; ii) a
lightweight image encoder module; iii) a fusion module; and iv) a
classification module. The experimental results obtained on a VQA benchmark
dataset demonstrate that our proposed LiT-4-RSVQA architecture provides
accurate VQA results while significantly reducing the computational
requirements on the executing hardware. Our code is publicly available at
this https URL

本文提出了一种基于 Transformer 轻量级架构的遥感视觉问答方法，名为 LiT-4-RSVQA，旨在有效且准确地回答针对遥感图像的自然语言问题，实验结果表明本方法可以显著减少计算资源使用并提供准确的 VQA 结果。