{"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/open-ended-visual-question-answering","title":"Open-Ended Visual Question-Answering","arxiv_id":"1610.02692","date":"2016-10-09","proceeding":null,"authors":["Issey Masuda","Santiago Pascual de la Puente","Xavier Giro-i-Nieto"],"abstract":"This thesis report studies methods to solve Visual Question-Answering (VQA)\ntasks with a Deep Learning framework. As a preliminary step, we explore Long\nShort-Term Memory (LSTM) networks used in Natural Language Processing (NLP) to\ntackle Question-Answering (text based). We then modify the previous model to\naccept an image as an input in addition to the question. For this purpose, we\nexplore the VGG-16 and K-CNN convolutional neural networks to extract visual\nfeatures from the image. These are merged with the word embedding or with a\nsentence embedding of the question to predict the answer. This work was\nsuccessfully submitted to the Visual Question Answering Challenge 2016, where\nit achieved a 53,62% of accuracy in the test dataset. The developed software\nhas followed the best programming practices and Python code style, providing a\nconsistent baseline in Keras for different configurations.","url_abs":"http://arxiv.org/abs/1610.02692v1","url_pdf":"http://arxiv.org/pdf/1610.02692v1.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":"open-ended-visual-question-answering","repo_url":"https://github.com/imatge-upc/vqa-2016-cvprw","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-embedding","task_name":"Sentence Embedding"},{"task_slug":"sentence-embedding-1","task_name":"Sentence-Embedding"},{"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}