{"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/simple-baseline-for-visual-question-answering","title":"Simple Baseline for Visual Question Answering","arxiv_id":"1512.02167","date":"2015-12-07","proceeding":null,"authors":["Bolei Zhou","Yuandong Tian","Sainbayar Sukhbaatar","Arthur Szlam","Rob Fergus"],"abstract":"We describe a very simple bag-of-words baseline for visual question\nanswering. This baseline concatenates the word features from the question and\nCNN features from the image to predict the answer. When evaluated on the\nchallenging VQA dataset [2], it shows comparable performance to many recent\napproaches using recurrent neural networks. To explore the strength and\nweakness of the trained model, we also provide an interactive web demo and\nopen-source code. .","url_abs":"http://arxiv.org/abs/1512.02167v2","url_pdf":"http://arxiv.org/pdf/1512.02167v2.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":"simple-baseline-for-visual-question-answering","repo_url":"https://github.com/metalbubble/VQAbaseline","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"simple-baseline-for-visual-question-answering","repo_url":"https://github.com/SkyOL5/VQA-CoAttention","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"simple-baseline-for-visual-question-answering","repo_url":"https://github.com/karunraju/VQA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"simple-baseline-for-visual-question-answering","repo_url":"https://github.com/miohana/vqa","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"simple-baseline-for-visual-question-answering","repo_url":"https://github.com/sidaw/nbsvm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"simple-baseline-for-visual-question-answering","repo_url":"https://github.com/sidgan/whats_in_a_question","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"simple-baseline-for-visual-question-answering","repo_url":"https://github.com/yikang-li/iqan","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"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-question-answering-on-coco-visual-1","task":"Visual Question Answering (VQA)","dataset":"COCO Visual Question Answering (VQA) real images 1.0 multiple choice","model":"iBOWIMG baseline","rank_in_archive_order":10,"of":10,"metrics":{"Percentage correct":"62.0"},"uses_additional_data":false},{"leaderboard":"/sota/visual-question-answering-on-coco-visual-4","task":"Visual Question Answering (VQA)","dataset":"COCO Visual Question Answering (VQA) real images 1.0 open ended","model":"iBOWIMG baseline","rank_in_archive_order":14,"of":14,"metrics":{"Percentage correct":"55.9"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1512.02167","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}