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Overview of the MediaEval 2022 predicting video memorability task

Sweeney, Lorin orcid logoORCID: 0000-0002-3427-1250, Constantin, Mihai Gabriel orcid logoORCID: 0000-0002-2312-6672, Demarty, Claire-Hélène, Fosco, Camilo, García Seco de Herrera, Alba orcid logoORCID: 0000-0002-6509-5325, Halder, Sebastian, Healy, Graham orcid logoORCID: 0000-0001-6429-6339, Ionescu, Bogdan, Matran-Fernandez, Ana orcid logoORCID: 0000-0002-8409-3747, Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 and Suntana, Mushfika (2023) Overview of the MediaEval 2022 predicting video memorability task. In: MediaEval’22: Multimedia Evaluation Workshop, 13–15 Jan 2023, Bergen, Norway & Online.

Abstract
This paper describes the 5th edition of the \textit{Predicting Video Memorability Task} as part of MediaEval2022. This year we have reorganised and simplified the task in order to lubricate a greater depth of inquiry. Similar to last year, two datasets are provided in order to facilitate generalisation, however, this year we have replaced the TRECVid2019 Video-to-Text dataset with the VideoMem dataset in order to remedy underlying data quality issues, and to prioritise short-term memorability prediction by elevating the Memento10k dataset as the primary dataset. Additionally, a fully fledged electroencephalography (EEG)-based prediction sub-task is introduced. In this paper, we outline the core facets of the task and its constituent sub-tasks; describing the datasets, evaluation metrics, and requirements for participant submissions.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Refereed:Yes
Subjects:Computer Science > Artificial intelligence
Computer Science > Machine learning
Computer Science > Multimedia systems
Computer Science > Digital video
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: Proceedings of MediaEval 2022. .
Official URL:https://2022.multimediaeval.com/paper2265.pdf
Copyright Information:© 2022 The Authors.
Funders:Science Foundation Ireland r Grant Number SFI/12/RC/2289_P2 & European Regional Development Fund, University of Essex Faculty of Science and Health Research Innovation and Support Fund, AI4Media, a European Excellence Centre for Media, Society and Democracy, H2020 ICT-48-2020, grant #951911
ID Code:27959
Deposited On:09 Jan 2023 12:40 by Alan Smeaton . Last Modified 19 Apr 2023 12:17
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