{"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/two-can-play-this-game-visual-dialog-with","title":"Two can play this Game: Visual Dialog with Discriminative Question Generation and Answering","arxiv_id":"1803.11186","date":"2018-03-29","proceeding":"CVPR 2018 6","authors":["Unnat Jain","Svetlana Lazebnik","Alexander Schwing"],"abstract":"Human conversation is a complex mechanism with subtle nuances. It is hence an\nambitious goal to develop artificial intelligence agents that can participate\nfluently in a conversation. While we are still far from achieving this goal,\nrecent progress in visual question answering, image captioning, and visual\nquestion generation shows that dialog systems may be realizable in the not too\ndistant future. To this end, a novel dataset was introduced recently and\nencouraging results were demonstrated, particularly for question answering. In\nthis paper, we demonstrate a simple symmetric discriminative baseline, that can\nbe applied to both predicting an answer as well as predicting a question. We\nshow that this method performs on par with the state of the art, even memory\nnet based methods. In addition, for the first time on the visual dialog\ndataset, we assess the performance of a system asking questions, and\ndemonstrate how visual dialog can be generated from discriminative question\ngeneration and question answering.","url_abs":"http://arxiv.org/abs/1803.11186v1","url_pdf":"http://arxiv.org/pdf/1803.11186v1.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":[],"tasks":[{"task_slug":"image-captioning","task_name":"Image Captioning"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"question-generation","task_name":"Question Generation"},{"task_slug":"question-generation","task_name":"Question-Generation"},{"task_slug":"visual-dialogue","task_name":"Visual Dialog"},{"task_slug":"visual-question-answering-1","task_name":"Visual Question Answering"},{"task_slug":"visual-question-answering","task_name":"Visual Question Answering (VQA)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/visual-dialog-on-visdial-v09-val","task":"Visual Dialog","dataset":"VisDial v0.9 val","model":"SF-QIH-se-2","rank_in_archive_order":7,"of":18,"metrics":{"MRR":"62.42","Mean Rank":"4.70","R@1":"48.55","R@10":"87.75","R@5":"78.96"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.11186","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}