{"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/the-color-of-the-cat-is-gray-1-million-full","title":"The Color of the Cat is Gray: 1 Million Full-Sentences Visual Question Answering (FSVQA)","arxiv_id":"1609.06657","date":"2016-09-21","proceeding":null,"authors":["Andrew Shin","Yoshitaka Ushiku","Tatsuya Harada"],"abstract":"Visual Question Answering (VQA) task has showcased a new stage of interaction\nbetween language and vision, two of the most pivotal components of artificial\nintelligence. However, it has mostly focused on generating short and repetitive\nanswers, mostly single words, which fall short of rich linguistic capabilities\nof humans. We introduce Full-Sentence Visual Question Answering (FSVQA)\ndataset, consisting of nearly 1 million pairs of questions and full-sentence\nanswers for images, built by applying a number of rule-based natural language\nprocessing techniques to original VQA dataset and captions in the MS COCO\ndataset. This poses many additional complexities to conventional VQA task, and\nwe provide a baseline for approaching and evaluating the task, on top of which\nwe invite the research community to build further improvements.","url_abs":"http://arxiv.org/abs/1609.06657v1","url_pdf":"http://arxiv.org/pdf/1609.06657v1.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":"question-answering","task_name":"Question Answering"},{"task_slug":"sentence","task_name":"Sentence"},{"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":[{"slug":"fsvqa","name":"FSVQA","full_name":"Full-Sentence Visual Question Answering"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1609.06657","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}