论文标题
K-VQG：通用性获取的知识感知的视觉问题生成
K-VQG: Knowledge-aware Visual Question Generation for Common-sense Acquisition
论文作者
论文摘要
视觉问题生成（VQG）是从图像中生成问题的任务。当人类问有关图像的问题时，他们的目标通常是获取一些新知识。但是，现有关于VQG的研究主要从答案或问题类别中解决了问题的生成，从而忽略了知识获取的目标。为了将知识获取的观点介绍到VQG中，我们构建了一个新颖的知识吸引的VQG数据集，称为K-VQG。这是第一个大型，人文注释的数据集，其中有关图像的问题与结构化知识有关。我们还开发了一种新的VQG模型，该模型可以编码和使用知识作为问题的目标。实验结果表明，我们的模型优于K-VQG数据集上的现有模型。
Visual Question Generation (VQG) is a task to generate questions from images. When humans ask questions about an image, their goal is often to acquire some new knowledge. However, existing studies on VQG have mainly addressed question generation from answers or question categories, overlooking the objectives of knowledge acquisition. To introduce a knowledge acquisition perspective into VQG, we constructed a novel knowledge-aware VQG dataset called K-VQG. This is the first large, humanly annotated dataset in which questions regarding images are tied to structured knowledge. We also developed a new VQG model that can encode and use knowledge as the target for a question. The experiment results show that our model outperforms existing models on the K-VQG dataset.
