{"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/ac-sum-gan-connecting-actor-critic-and","title":"AC-SUM-GAN: Connecting Actor-Critic and Generative Adversarial Networks for Unsupervised Video Summarization","arxiv_id":null,"date":"2020-11-16","proceeding":"IEEE Transactions on Circuits and Systems for Video Technology 2020 11","authors":["Evlampios Apostolidis","Eleni Adamantidou","Alexandros I. Metsai","Vasileios Mezaris","Ioannis Patras"],"abstract":"This paper presents a new method for unsupervised video summarization. The proposed architecture embeds an Actor-Critic model into a Generative Adversarial Network and formulates the selection of important video fragments (that will be used to form the summary) as a sequence generation task. The Actor and the Critic take part in a game that incrementally leads to the selection of the video key-fragments, and their choices at each step of the game result in a set of rewards from the Discriminator. The designed training workflow allows the Actor and Critic to discover a space of actions and automatically learn a policy for key-fragment selection. Moreover, the introduced criterion for choosing the best model after the training ends, enables the automatic selection of proper values for parameters of the training process that are not learned from the data (such as the regularization factor σ). Experimental evaluation on two benchmark datasets (SumMe and TVSum) demonstrates that the proposed AC-SUM-GAN model performs consistently well and gives SoA results in comparison to unsupervised methods, that are also competitive with respect to supervised methods.","url_abs":"https://ieeexplore.ieee.org/document/9259058","url_pdf":"https://ieeexplore.ieee.org/document/9259058","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":"ac-sum-gan-connecting-actor-critic-and","repo_url":"https://github.com/e-apostolidis/AC-SUM-GAN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"unsupervised-video-summarization","task_name":"Unsupervised Video Summarization"},{"task_slug":"video-summarization","task_name":"Video Summarization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/unsupervised-video-summarization-on-summe","task":"Unsupervised Video Summarization","dataset":"SumMe","model":"AC-SUM-GAN","rank_in_archive_order":6,"of":10,"metrics":{"F1-score":"50.8","Parameters (M)":"26.75","training time (s)":"2825"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-video-summarization-on-tvsum","task":"Unsupervised Video Summarization","dataset":"TvSum","model":"AC-SUM-GAN","rank_in_archive_order":3,"of":8,"metrics":{"F1-score":"60.6","Parameters (M)":"26.75","training time (s)":"9380"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}