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Overview of MediaEval 2020 predicting media memorability task: what makes a video memorable?

de Herrera, Alba G. Seco ORCID: 0000-0002-6509-5325, Kiziltepe, Rukiye Savran ORCID: 0000-0002-3862-7621, Chamberlain, Jon ORCID: 0000-0002-6947-8964, Constantin, Mihai Gabriel ORCID: 0000-0002-2312-6672, Demarty, Claire-Hélène, Doctor, Faiyaz ORCID: 0000-0002-8412-5489, Ionescu, Bogdan ORCID: 0000-0003-4112-5769 and Smeaton, Alan F. ORCID: 0000-0003-1028-8389 (2020) Overview of MediaEval 2020 predicting media memorability task: what makes a video memorable? In: MediaEval 2020 Multimedia Benchmark Workshop, 14-15 Dec 2020, Online.

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Abstract

This paper describes the MediaEval 2020 Predicting Media Memorability task. After first being proposed at MediaEval 2018, the Predicting Media Memorability task is in its 3rd edition this year, as the prediction of short-term and long-term video memorability (VM) remains a challenging task. In 2020, the format remained the same as in previous editions. This year the videos are a subset of the TRECVid 2019 Video-to-Text dataset, containing more action rich video content as compared with the 2019 task. In this paper a description of some aspects of this task is provided, including its main characteristics, a description of the collection, the ground truth dataset, evaluation metrics and the requirements for participants’ run submissions.

Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Refereed:Yes
Uncontrolled Keywords:Memory
Subjects: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 the MediaEval 2020 Workshop. . CEUR.
Publisher:CEUR
Official URL:http://ceur-ws.org/Vol-2882/
Copyright Information:© 2020 The Authors. Open Access (CC BY 4.0)
Funders:NIST Award No. 60NANB19D155, Science Foundation Ireland SFI/12/RC/2289_P2 and under project AI4Media, European ExcellenceCentre for Media, Society and Democracy, H2020 ICT-48-2020, grant 951911
ID Code:25298
Deposited On:05 Aug 2021 10:10 by Alan Smeaton . Last Modified 08 Nov 2021 15:14

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