{"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/visual-entailment-task-for-visually-grounded","title":"Visual Entailment Task for Visually-Grounded Language Learning","arxiv_id":"1811.10582","date":"2018-11-26","proceeding":null,"authors":["Ning Xie","Farley Lai","Derek Doran","Asim Kadav"],"abstract":"We introduce a new inference task - Visual Entailment (VE) - which differs\nfrom traditional Textual Entailment (TE) tasks whereby a premise is defined by\nan image, rather than a natural language sentence as in TE tasks. A novel\ndataset SNLI-VE (publicly available at https://github.com/necla-ml/SNLI-VE) is\nproposed for VE tasks based on the Stanford Natural Language Inference corpus\nand Flickr30k. We introduce a differentiable architecture called the\nExplainable Visual Entailment model (EVE) to tackle the VE problem. EVE and\nseveral other state-of-the-art visual question answering (VQA) based models are\nevaluated on the SNLI-VE dataset, facilitating grounded language understanding\nand providing insights on how modern VQA based models perform.","url_abs":"http://arxiv.org/abs/1811.10582v2","url_pdf":"http://arxiv.org/pdf/1811.10582v2.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":"visual-entailment-task-for-visually-grounded","repo_url":"https://github.com/necla-ml/SNLI-VE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"grounded-language-learning","task_name":"Grounded language learning"},{"task_slug":"natural-language-inference","task_name":"Natural Language Inference"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"visual-entailment","task_name":"Visual Entailment"},{"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":"https://app.syntology.ai/?focus=1811.10582","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}