{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multimodal-differential-network-for-visual","title":"Multimodal Differential Network for Visual Question Generation","arxiv_id":"1808.03986","date":"2018-08-12","proceeding":"EMNLP 2018 10","authors":["Badri N. Patro","Sandeep Kumar","Vinod K. Kurmi","Vinay P. Namboodiri"],"abstract":"Generating natural questions from an image is a semantic task that requires using visual and language modality to learn multimodal representations. Images can have multiple visual and language contexts that are relevant for generating questions namely places, captions, and tags. In this paper, we propose the use of exemplars for obtaining the relevant context. We obtain this by using a Multimodal Differential Network to produce natural and engaging questions. The generated questions show a remarkable similarity to the natural questions as validated by a human study. Further, we observe that the proposed approach substantially improves over state-of-the-art benchmarks on the quantitative metrics (BLEU, METEOR, ROUGE, and CIDEr).","url_abs":"https://arxiv.org/abs/1808.03986v2","url_pdf":"https://arxiv.org/pdf/1808.03986v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multimodal-differential-network-for-visual","repo_url":"https://github.com/badripatro/MDN-VQG","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"natural-questions","task_name":"Natural Questions"},{"task_slug":"question-generation","task_name":"Question Generation"},{"task_slug":"question-generation","task_name":"Question-Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/question-generation-on-coco-visual-question","task":"Question Generation","dataset":"COCO Visual Question Answering (VQA) real images 1.0 open ended","model":"MDN","rank_in_archive_order":1,"of":4,"metrics":{"BLEU-1":"65.1"},"uses_additional_data":false},{"leaderboard":"/sota/question-generation-on-visual-question","task":"Question Generation","dataset":"Visual Question Generation","model":"MDN","rank_in_archive_order":1,"of":1,"metrics":{"BLEU-1":"36.0"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1808.03986","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}