遥感中的视觉-语言模型：当前进展与未来趋势
Vision-Language Models in Remote Sensing: Current Progress and Future Trends
AI总结：
本文系统综述了视觉-语言模型在遥感领域的研究现状，指出其相比传统视觉方法能更好地理解图像语义关系，并总结了最新进展、挑战与未来研究方向。
摘要：
ChatGPT和GPT-4的显著成就引发了人们对面向通用人工智能（AGI）的大语言模型领域的广泛兴趣与研究热潮。这些模型提供了接近人类思维的智能解决方案，使我们能够利用通用人工智能解决各种应用中的问题。然而，在遥感（RS）领域，关于AGI实现的科学文献仍然相对匮乏。现有的遥感人工智能相关研究主要集中于视觉理解任务，而忽视了对目标及其关系的语义理解。这正是视觉-语言模型的优势所在，因为它们能够对图像及其关联的文本描述进行推理，从而更深入地理解底层语义。视觉-语言模型可以超越遥感图像的视觉识别，建模语义关系，并生成图像的自然语言描述。这使得它们更适合需要视觉和文本理解的任务，例如图像描述和视觉问答。本文对遥感中视觉-语言模型的研究进行了全面综述，总结了最新进展，突出了挑战，并指出了潜在的研究机会。
英文摘要：
The remarkable achievements of ChatGPT and GPT-4 have sparked a wave of interest and research in the field of large language models for Artificial General Intelligence (AGI). These models provide intelligent solutions close to human thinking, enabling us to use general artificial intelligence to solve problems in various applications. However, in remote sensing (RS), the scientific literature on the implementation of AGI remains relatively scant. Existing AI-related research in remote sensing primarily focuses on visual understanding tasks while neglecting the semantic understanding of the objects and their relationships. This is where vision-language models excel, as they enable reasoning about images and their associated textual descriptions, allowing for a deeper understanding of the underlying semantics. Vision-language models can go beyond visual recognition of RS images, model semantic relationships, and generate natural language descriptions of the image. This makes them better suited for tasks requiring visual and textual understanding, such as image captioning, and visual question answering. This paper provides a comprehensive review of the research on vision-language models in remote sensing, summarizing the latest progress, highlighting challenges, and identifying potential research opportunities.
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。
