{"type":"ScholarlyArticle","title":"Automatic Generation of Grounded Visual Questions","authors":[{"name":"Shijie Zhang","slug":"shijie-zhang","given":"Shijie","family":"Zhang","affiliation":"# School of Computer Science and Technology , Tianjin University , Tianjin , China"},{"name":"Lizhen Qu","slug":"lizhen-qu","given":"Lizhen","family":"Qu","affiliation":"¶ Australian National University , Canberra , Australia"},{"name":"Shaodi You","slug":"shaodi-you","given":"Shaodi","family":"You","affiliation":"§ Data61 - CSIRO , Canberra , Australia"},{"name":"Zhenglu Yang","slug":"zhenglu-yang","given":"Zhenglu","family":"Yang","affiliation":"† College of Computer and Control Engineering , Nankai University , Tianjin , China"},{"name":"Jiawan Zhang","slug":"jiawan-zhang","given":"Jiawan","family":"Zhang","affiliation":"# School of Computer Science and Technology , Tianjin University , Tianjin , China"}],"abstract":"In this paper, we propose the first model to be able to generate visually grounded questions with diverse types for a single image. Visual question generation is an emerging topic which aims to ask questions in natural language based on visual input. To the best of our knowledge, it lacks automatic methods to generate meaningful questions with various types for the same visual input.\r\n\r\nTo circumvent the problem, we propose a model that automatically generates visually grounded questions with varying types. Our model takes as input both images and the captions generated by a dense caption model, samples the most probable question types, and generates the questions in sequel. The experimental results on two real world datasets show that our model outperforms the strongest baseline in terms of both correctness and diversity with a wide margin.","doi":"10.24963/ijcai.2017/592","url":"https://ijcai.org/proceedings/2017/592","pdf":"https://ijcai.org/proceedings/2017/0592.pdf","markdown":"https://ijcai.org/proceedings/2017/592.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/592","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Natural Language Processing","track":"Main track","number":592,"pages":{"first":4235,"last":4243},"keywords":["Robotics and Vision: Vision and Perception","Natural Language Processing: Question Answering"],"videos":[],"references":[{"position":1,"raw":"[Antol et al., 2015] Stanislaw Antol, Aishwarya Agrawal, Ji- asen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zit- nick, and Devi Parikh. 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