SEMT：用于遥感图像描述任务的静态扩展网格Transformer网络架构
SEMT: Static-Expansion-Mesh Transformer Network Architecture for Remote Sensing Image Captioning
AI总结：
本文提出SEMT遥感图像描述网络架构，融合静态扩展、记忆增强自注意力与网格Transformer技术，在UCM-Caption和NWPU-Caption数据集上多数指标超越现有最优系统。
摘要：
图像描述已成为计算机视觉与自然语言处理交叉领域中的一项关键任务，能够从视觉内容自动生成描述性文本。在遥感背景下，图像描述在解读海量复杂卫星影像方面发挥着重要作用，可辅助环境监测、灾害评估和城市规划等应用。这促使我们在本文中提出一种基于Transformer的遥感图像描述（RSIC）网络架构，其中评估并集成了静态扩展、记忆增强自注意力和网格Transformer等多种技术。我们使用UCM-Caption和NWPU-Caption两个基准遥感图像数据集对所提模型进行评估。我们最优的模型在大多数评价指标上超越了当前最先进的系统，展示了应用于实际遥感图像系统的潜力。
英文摘要：
Image captioning has emerged as a crucial task in the intersection of computer vision and natural language processing, enabling automated generation of descriptive text from visual content. In the context of remote sensing, image captioning plays a significant role in interpreting vast and complex satellite imagery, aiding applications such as environmental monitoring, disaster assessment, and urban planning. This motivates us, in this paper, to present a transformer based network architecture for remote sensing image captioning (RSIC) in which multiple techniques of Static Expansion, Memory-Augmented Self-Attention, Mesh Transformer are evaluated and integrated. We evaluate our proposed models using two benchmark remote sensing image datasets of UCM-Caption and NWPU-Caption. Our best model outperforms the state-of-the-art systems on most of evaluation metrics, which demonstrates potential to apply for real-life remote sensing image systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
