{"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/tutorial-on-answering-questions-about-images","title":"Tutorial on Answering Questions about Images with Deep Learning","arxiv_id":"1610.01076","date":"2016-10-04","proceeding":null,"authors":["Mateusz Malinowski","Mario Fritz"],"abstract":"Together with the development of more accurate methods in Computer Vision and\nNatural Language Understanding, holistic architectures that answer on questions\nabout the content of real-world images have emerged. In this tutorial, we build\na neural-based approach to answer questions about images. We base our tutorial\non two datasets: (mostly on) DAQUAR, and (a bit on) VQA. With small tweaks the\nmodels that we present here can achieve a competitive performance on both\ndatasets, in fact, they are among the best methods that use a combination of\nLSTM with a global, full frame CNN representation of an image. We hope that\nafter reading this tutorial, the reader will be able to use Deep Learning\nframeworks, such as Keras and introduced Kraino, to build various architectures\nthat will lead to a further performance improvement on this challenging task.","url_abs":"http://arxiv.org/abs/1610.01076v1","url_pdf":"http://arxiv.org/pdf/1610.01076v1.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":"tutorial-on-answering-questions-about-images","repo_url":"https://github.com/mateuszmalinowski/visual_turing_test-tutorial","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"natural-language-understanding","task_name":"Natural Language Understanding"},{"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}