{"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/modular-memorability-tiered-representations","title":"Modular Memorability: Tiered Representations for Video Memorability Prediction","arxiv_id":null,"date":"2023-01-01","proceeding":"CVPR 2023 1","authors":["Théo Dumont","Juan Segundo Hevia","Camilo L. Fosco"],"abstract":"    The question of how to best estimate the memorability of visual content is currently a source of debate in the memorability community. In this paper, we propose to explore how different key properties of images and videos affect their consolidation into memory. We analyze the impact of several features and develop a model that emulates the most important parts of a proposed \"pathway to memory\": a simple but effective way of representing the different hurdles that new visual content needs to surpass to stay in memory. This framework leads to the construction of our M3-S model, a novel memorability network that processes input videos in a modular fashion. Each module of the network emulates one of the four key steps of the pathway to memory: raw encoding, scene understanding, event understanding and memory consolidation. We find that the different representations learned by our modules are non-trivial and substantially different from each other. Additionally, we observe that certain representations tend to perform better at the task of memorability prediction than others, and we introduce an in-depth ablation study to support our results. Our proposed approach surpasses the state of the art on the two largest video memorability datasets and opens the door to new applications in the field.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2023/html/Dumont_Modular_Memorability_Tiered_Representations_for_Video_Memorability_Prediction_CVPR_2023_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2023/papers/Dumont_Modular_Memorability_Tiered_Representations_for_Video_Memorability_Prediction_CVPR_2023_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":"modular-memorability-tiered-representations","repo_url":"https://github.com/tekal-ai/modular-memorability","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}