{"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/translating-videos-to-natural-language-using","title":"Translating Videos to Natural Language Using Deep Recurrent Neural Networks","arxiv_id":"1412.4729","date":"2014-12-15","proceeding":"HLT 2015 5","authors":["Subhashini Venugopalan","Huijuan Xu","Jeff Donahue","Marcus Rohrbach","Raymond Mooney","Kate Saenko"],"abstract":"Solving the visual symbol grounding problem has long been a goal of\nartificial intelligence. The field appears to be advancing closer to this goal\nwith recent breakthroughs in deep learning for natural language grounding in\nstatic images. In this paper, we propose to translate videos directly to\nsentences using a unified deep neural network with both convolutional and\nrecurrent structure. Described video datasets are scarce, and most existing\nmethods have been applied to toy domains with a small vocabulary of possible\nwords. By transferring knowledge from 1.2M+ images with category labels and\n100,000+ images with captions, our method is able to create sentence\ndescriptions of open-domain videos with large vocabularies. We compare our\napproach with recent work using language generation metrics, subject, verb, and\nobject prediction accuracy, and a human evaluation.","url_abs":"http://arxiv.org/abs/1412.4729v3","url_pdf":"http://arxiv.org/pdf/1412.4729v3.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":"translating-videos-to-natural-language-using","repo_url":"https://github.com/nasib-ullah/video-captioning-models-in-Pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"text-generation","task_name":"Text Generation"},{"task_slug":"video-captioning","task_name":"Video Captioning"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1412.4729","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}