{"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/ffnet-video-fast-forwarding-via-reinforcement","title":"FFNet: Video Fast-Forwarding via Reinforcement Learning","arxiv_id":"1805.02792","date":"2018-05-08","proceeding":"CVPR 2018 6","authors":["Shuyue Lan","Rameswar Panda","Qi Zhu","Amit K. Roy-Chowdhury"],"abstract":"For many applications with limited computation, communication, storage and\nenergy resources, there is an imperative need of computer vision methods that\ncould select an informative subset of the input video for efficient processing\nat or near real time. In the literature, there are two relevant groups of\napproaches: generating a trailer for a video or fast-forwarding while\nwatching/processing the video. The first group is supported by video\nsummarization techniques, which require processing of the entire video to\nselect an important subset for showing to users. In the second group, current\nfast-forwarding methods depend on either manual control or automatic adaptation\nof playback speed, which often do not present an accurate representation and\nmay still require processing of every frame. In this paper, we introduce\nFastForwardNet (FFNet), a reinforcement learning agent that gets inspiration\nfrom video summarization and does fast-forwarding differently. It is an online\nframework that automatically fast-forwards a video and presents a\nrepresentative subset of frames to users on the fly. It does not require\nprocessing the entire video, but just the portion that is selected by the\nfast-forward agent, which makes the process very computationally efficient. The\nonline nature of our proposed method also enables the users to begin\nfast-forwarding at any point of the video. Experiments on two real-world\ndatasets demonstrate that our method can provide better representation of the\ninput video with much less processing requirement.","url_abs":"http://arxiv.org/abs/1805.02792v1","url_pdf":"http://arxiv.org/pdf/1805.02792v1.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":"ffnet-video-fast-forwarding-via-reinforcement","repo_url":"https://github.com/shuyueL/FFNet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"video-summarization","task_name":"Video Summarization"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}