面向遥感图像的知识感知视觉问题生成
Knowledge-aware Visual Question Generation for Remote Sensing Images
AI总结：
本文提出KRSVQG模型，通过结合外部知识生成更丰富、基于图像和领域知识的问题，提升遥感图像问答系统的性能。
摘要：
随着遥感图像档案的快速发展，通过图像提问来获取特定信息或进行图像检索已成为一种有效方法。然而，自动生成功能图像相关的问题往往过于简单且基于模板，这阻碍了问答或视觉对话系统的实际部署。为丰富和多样化问题，我们提出了一种知识感知的遥感视觉问题生成模型KRSVQG，该模型结合与图像内容相关的外部知识以提高生成问题的质量和上下文理解。该模型以图像和相关的外部知识三元组作为输入，并利用图像描述作为中介表示来增强生成问题的图像基础。为了评估KRSVQG的性能，我们使用了两个我们手动标注的数据集：NWPU-300和TextRS-300。在这些数据集上的结果表明，KRSVQG优于现有方法，并导致基于图像和领域知识的知识丰富问题。
英文摘要：
With the rapid development of remote sensing image archives, asking questions about images has become an effective way of gathering specific information or performing image retrieval. However, automatically generated image-based questions tend to be simplistic and template-based, which hinders the real deployment of question answering or visual dialogue systems. To enrich and diversify the questions, we propose a knowledge-aware remote sensing visual question generation model, KRSVQG, that incorporates external knowledge related to the image content to improve the quality and contextual understanding of the generated questions. The model takes an image and a related knowledge triplet from external knowledge sources as inputs and leverages image captioning as an intermediary representation to enhance the image grounding of the generated questions. To assess the performance of KRSVQG, we utilized two datasets that we manually annotated: NWPU-300 and TextRS-300. Results on these two datasets demonstrate that KRSVQG outperforms existing methods and leads to knowledge-enriched questions, grounded in both image and domain knowledge.
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。
