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Overview of The MediaEval 2021 Predicting Media Memorability Task

Kiziltepe, Rukiye Savran orcid logoORCID: 0000-0002-3862-7621, Constantin, Mihai Gabriel orcid logoORCID: 0000-0002-2312-6672, Demarty, Claire-Hélène, Healy, Graham orcid logoORCID: 0000-0001-6429-6339, Fosco, Camilo, García Seco de Herrera, Alba orcid logoORCID: 0000-0002-6509-5325, Halder, Sebastian orcid logoORCID: 0000-0003-1017-3696, Ionescu, Bogdan, Matran-Fernandez, Ana orcid logoORCID: 0000-0002-8409-3747, Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 and Sweeney, Lorin orcid logoORCID: 0000-0002-3427-1250 (2021) Overview of The MediaEval 2021 Predicting Media Memorability Task. In: MediaEval: MediaEval Benchmarking Initiative for Multimedia Evaluation, 2021, 13-15 Dec 2021, Online.

Abstract
This paper describes the MediaEval 2021 Predicting Media Memorability}task, which is in its 4th edition this year, as the prediction of short-term and long-term video memorability remains a challenging task. In 2021, two datasets of videos are used: first, a subset of the TRECVid 2019 Video-to-Text dataset; second, the Memento10K dataset in order to provide opportunities to explore cross-dataset generalisation. In addition, an Electroencephalography (EEG)-based prediction pilot subtask is introduced. In this paper, we outline the main aspects of the task and describe the datasets, evaluation metrics, and requirements for participants' submissions.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
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: Working Notes Proceedings of the MediaEval 2021 Workshop. . CEUR-WS.
Publisher:CEUR-WS
Official URL:https://2021.multimediaeval.com/
Copyright Information:© 2021 The Authors. Open Access (CC-BY-4.0)
Funders:Science Foundation Ireland grant number SFI/12/RC/2289_P2., H2020 ICT-48-2020, grant 951911 AI4Media, NIST Award No. 60NANB19D155, Turkish Ministry of National Education
ID Code:26533
Deposited On:13 Dec 2021 14:01 by Alan Smeaton . Last Modified 27 Jul 2022 10:14
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