{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T21:37:39Z","timestamp":1768340259614,"version":"3.49.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>Visual question generation aims at asking questions about an image automatically. Existing research works on this topic usually generate a single question for each given image without considering the issue of diversity. In this paper, we propose a question type driven framework to produce multiple questions for a given image with different focuses. In our framework, each question is constructed following the guidance of a sampled question type in a sequence-to-sequence fashion. To diversify the generated questions, a novel conditional variational auto-encoder is introduced to generate multiple questions with a specific question type. Moreover, we design a strategy to conduct the question type distribution learning for each image to select the final questions. Experimental results on three benchmark datasets show that our framework outperforms the state-of-the-art approaches in terms of both relevance and diversity.<\/jats:p>","DOI":"10.24963\/ijcai.2018\/563","type":"proceedings-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T01:49:10Z","timestamp":1530755350000},"page":"4048-4054","source":"Crossref","is-referenced-by-count":25,"title":["A Question Type Driven Framework to Diversify Visual Question Generation"],"prefix":"10.24963","author":[{"given":"Zhihao","family":"Fan","sequence":"first","affiliation":[{"name":"School of Data Science, Fudan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongyu","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Data Science, Fudan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Piji","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanyan","family":"Lan","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"},{"name":"School of Data Science, Fudan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuanjing","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Fudan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}","theme":"Artificial Intelligence","location":"Stockholm, Sweden","acronym":"IJCAI-2018","number":"27","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2018,7,13]]},"end":{"date-parts":[[2018,7,19]]}},"container-title":["Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T01:53:58Z","timestamp":1530755638000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2018\/563"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2018\/563","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}