{"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/predicting-event-memorability-from-contextual","title":"Predicting Event Memorability from Contextual Visual Semantics","arxiv_id":null,"date":"2021-12-01","proceeding":"NeurIPS 2021 12","authors":["Qianli Xu","Fen Fang","Ana Molino","Vigneshwaran Subbaraju","Joo-Hwee Lim"],"abstract":"Episodic event memory is a key component of human cognition. Predicting event memorability,i.e., to what extent an event is recalled, is a tough challenge in memory research and has profound implications for artificial intelligence. In this study, we investigate factors that affect event memorability according to a cued recall process. Specifically, we explore whether event memorability is contingent on the event context, as well as the intrinsic visual attributes of image cues. We design a novel experiment protocol and conduct a large-scale experiment with 47 elder subjects over 3 months.  Subjects’ memory of life events is tested in a cued recall process. Using advanced visual analytics methods, we build a first-of-its-kind event memorability dataset (called R3) with rich information about event context and visual semantic features. Furthermore, we propose a contextual event memory network (CEMNet) that tackles multi-modal input to predict item-wise event memorability, which outperforms competitive benchmarks.  The findings inform deeper understanding of episodic event memory, and open up a new avenue for prediction of human episodic memory.  Source code is available at https://github.com/ffzzy840304/Predicting-Event-Memorability.","url_abs":"http://proceedings.neurips.cc/paper/2021/hash/bcc2bdb799f873f02080ae277f291da1-Abstract.html","url_pdf":"http://proceedings.neurips.cc/paper/2021/file/bcc2bdb799f873f02080ae277f291da1-Paper.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":"predicting-event-memorability-from-contextual","repo_url":"https://github.com/ffzzy840304/predicting-event-memorability","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[{"method_slug":"memory-network","method_name":"Memory Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}