Visual question generation (VQG) is an interesting problem that has recently received attention. The task of VQG involves generating meaningful questions based on the input image. It is a multi-modal problem involving image understanding and natural language generation, especially using deep learning methods. VQG can be considered as complementary task of visual question answering. In this article, we review the current state of VQG in terms of methods to understand the problem, existing datasets to train the VQG model, evaluation metrics, and algorithms to handle the problem. Finally, we discuss the challenges that need to be conquered and the possible future directions for an effective VQG.

中文翻译：

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视觉问题生成

视觉问题生成（VQG）是一个最近受到关注的有趣问题。VQG 的任务涉及根据输入图像生成有意义的问题。这是一个涉及图像理解和自然语言生成的多模态问题，尤其是使用深度学习方法。VQG 可以被认为是视觉问答的补充任务。在本文中，我们从理解问题的方法、训练 VQG 模型的现有数据集、评估指标和处理问题的算法等方面回顾了 VQG 的当前状态。最后，我们讨论了需要克服的挑战以及有效的 VQG 可能的未来方向。