Skip to main content
DORAS
DCU Online Research Access Service
Login (DCU Staff Only)
Experiences from the MediaEval predicting media memorability task

García Seco de Herrera, Alba ORCID: 0000-0002-6509-5325, Constantin, Mihai Gabriel ORCID: 0000-0002-2312-6672, Demarty, Claire-Hélène, Fosco, Camilo, Halder, Sebastian ORCID: 0000-0003-1017-3696, Healy, Graham ORCID: 0000-0001-6429-6339, Ionescu, Bogdan, Matran-Fernandez, Ana ORCID: 0000-0002-8409-3747, Smeaton, Alan F. ORCID: 0000-0003-1028-8389, Sultana, Mushfika and Sweeney, Lorin ORCID: 0000-0002-3427-1250 (2022) Experiences from the MediaEval predicting media memorability task. In: The NeurIPS Memory in Artificial and Real Intelligence (MemARI) Workshop, 2 Dec 2022, New Orleans, USA.

Full text available as:

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
209kB

Abstract

The Predicting Media Memorability task in the MediaEval evaluation campaign has been running annually since 2018 and several different tasks and data sets have been used in this time. This has allowed us to compare the performance of many memorability prediction techniques on the same data and in a reproducible way and to refine and improve on those techniques. The resources created to compute media memorability are now being used by researchers well beyond the actual evaluation campaign. In this paper we present a summary of the task, including the collective lessons we have learned for the research community.

Item Type:Conference or Workshop Item (Poster)
Event Type:Workshop
Refereed:Yes
Uncontrolled Keywords:video memorability
Subjects:Computer Science > Artificial intelligence
Computer Science > Machine learning
Computer Science > Multimedia systems
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Copyright Information:© 2022 The Authors
Funders:University of Essex Faculty of Science and Health Research Innovation and Support Fund, Science Foundation Ireland Grant Number SFI/12/RC/2289_P2, European Regional Development Fund, AI4Media, a European Excellence Centre for Media, Society and Democracy, H2020 ICT-48-2020, grant #951911
ID Code:27948
Deposited On:17 Jan 2023 11:34 by Alan Smeaton . Last Modified 17 Jan 2023 14:57

Downloads

Downloads per month over past year

Archive Staff Only: edit this record

  • Student Email
  • Staff Email
  • Student Apps
  • Staff Apps
  • Loop
  • Disclaimer
  • Privacy
  • Contact Us