{"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/vibiknet-visual-bidirectional-kernelized","title":"VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering","arxiv_id":"1612.03628","date":"2016-12-12","proceeding":null,"authors":["Marc Bolaños","Álvaro Peris","Francisco Casacuberta","Petia Radeva"],"abstract":"In this paper, we address the problem of visual question answering by\nproposing a novel model, called VIBIKNet. Our model is based on integrating\nKernelized Convolutional Neural Networks and Long-Short Term Memory units to\ngenerate an answer given a question about an image. We prove that VIBIKNet is\nan optimal trade-off between accuracy and computational load, in terms of\nmemory and time consumption. We validate our method on the VQA challenge\ndataset and compare it to the top performing methods in order to illustrate its\nperformance and speed.","url_abs":"http://arxiv.org/abs/1612.03628v1","url_pdf":"http://arxiv.org/pdf/1612.03628v1.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":"vibiknet-visual-bidirectional-kernelized","repo_url":"https://github.com/MarcBS/VIBIKNet","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}