Dual Learning for Visual Question Generation

Recently, automatic answering of visually related questions (VQA) has gained a lot of attention in computer vision community. However, there is little work on automatically generating questions for images (VQG). Actually, VQG itself closes the loop to question-answering and diverse questions, which is useful to the research on VQA. Motivated by the assumption that learning to answer questions may boost the question generation, in this paper, we introduce the VQA task as the complementary of our primary VQG task, and propose a novel model that uses dual learning framework to jointly learn the dual tasks. In the framework, we devise an agent for VQG and VQA with pre-trained models respectively, and the learning tasks of the two agents form a closed loop, whose objectives are optimized together to guide each other via a reinforcement learning process. Specific rewards for each task are designed to update the models of the agents with policy gradient method. The relation of these two tasks can be exploited to further improve the performance of the primary VQG task. Extensive experiments conducted on two large-scale datasets show that the proposed method is capable to generate grounded visual questions of sufficient coverage and outperforms previous VQG methods on standard measures.