VRSBench：用于遥感图像理解的通用视觉-语言基准数据集
VRSBench: A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding
AI总结：
针对现有遥感视觉-语言数据集任务单一、信息不足等问题，提出通用基准VRSBench，包含近3万图像及丰富标注，支持多任务训练评估，推动遥感大模型发展。
摘要：
我们引入了一个新的基准，旨在推进面向遥感图像的通用大规模视觉-语言模型的发展。尽管已有多个遥感领域的视觉-语言数据集被提出以实现这一目标，但现有数据集通常针对单一任务定制，缺乏详细的对象信息，或存在质量控制不足的问题。针对这些改进机会，我们提出了一个用于遥感图像理解的通用视觉-语言基准，称为VRSBench。该基准包含29,614张图像，配有29,614条经过人工验证的详细描述、52,472个对象引用以及123,221个问答对。它支持视觉-语言模型在广泛的遥感图像理解任务上的训练和评估。我们进一步在此基准上评估了最先进的模型，涵盖三个视觉-语言任务：图像描述、视觉定位和视觉问答。我们的工作旨在为遥感领域先进视觉-语言模型的发展做出重要贡献。数据和代码可在https://github.com/lx709/VRSBench获取。
英文摘要：
We introduce a new benchmark designed to advance the development of general-purpose, large-scale vision-language models for remote sensing images. Although several vision-language datasets in remote sensing have been proposed to pursue this goal, existing datasets are typically tailored to single tasks, lack detailed object information, or suffer from inadequate quality control. Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench. This benchmark comprises 29,614 images, with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs. It facilitates the training and evaluation of vision-language models across a broad spectrum of remote sensing image understanding tasks. We further evaluated state-of-the-art models on this benchmark for three vision-language tasks: image captioning, visual grounding, and visual question answering. Our work aims to significantly contribute to the development of advanced vision-language models in the field of remote sensing. The data and code can be accessed at https://github.com/lx709/VRSBench.
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。
