  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.
