{"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/game-of-sketches-deep-recurrent-models-of","title":"Game of Sketches: Deep Recurrent Models of Pictionary-style Word Guessing","arxiv_id":"1801.09356","date":"2018-01-29","proceeding":null,"authors":["Ravi Kiran Sarvadevabhatla","Shiv Surya","Trisha Mittal","Venkatesh Babu Radhakrishnan"],"abstract":"The ability of intelligent agents to play games in human-like fashion is\npopularly considered a benchmark of progress in Artificial Intelligence.\nSimilarly, performance on multi-disciplinary tasks such as Visual Question\nAnswering (VQA) is considered a marker for gauging progress in Computer Vision.\nIn our work, we bring games and VQA together. Specifically, we introduce the\nfirst computational model aimed at Pictionary, the popular word-guessing social\ngame. We first introduce Sketch-QA, an elementary version of Visual Question\nAnswering task. Styled after Pictionary, Sketch-QA uses incrementally\naccumulated sketch stroke sequences as visual data. Notably, Sketch-QA involves\nasking a fixed question (\"What object is being drawn?\") and gathering\nopen-ended guess-words from human guessers. We analyze the resulting dataset\nand present many interesting findings therein. To mimic Pictionary-style\nguessing, we subsequently propose a deep neural model which generates\nguess-words in response to temporally evolving human-drawn sketches. Our model\neven makes human-like mistakes while guessing, thus amplifying the human\nmimicry factor. We evaluate our model on the large-scale guess-word dataset\ngenerated via Sketch-QA task and compare with various baselines. We also\nconduct a Visual Turing Test to obtain human impressions of the guess-words\ngenerated by humans and our model. Experimental results demonstrate the promise\nof our approach for Pictionary and similarly themed games.","url_abs":"http://arxiv.org/abs/1801.09356v1","url_pdf":"http://arxiv.org/pdf/1801.09356v1.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":"game-of-sketches-deep-recurrent-models-of","repo_url":"https://github.com/val-iisc/sketchguess","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"question-answering","task_name":"Question Answering"},{"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":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}