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Predicting media memorability: comparing visual, textual and auditory features

Sweeney, Lorin orcid logoORCID: 0000-0002-3427-1250, Healy, Graham orcid logoORCID: 0000-0001-6429-6339 and Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 (2021) Predicting media memorability: comparing visual, textual and auditory features. In: MediaEval 2021 Multimedia Benchmark, 13-15 Dec 2021, Online.

Abstract
This paper describes our approach to the Predicting Media Memorability task in MediaEval 2021, which aims to address the question of media memorability by setting the task of automatically predicting video memorability. This year we tackle the task from a comparative standpoint, looking to gain deeper insights into each of three explored modalities, and using our results from last year's submission (2020) as a point of reference. Our best performing short-term memorability model (0.132) tested on the TRECVid2019 dataset---just like last year---was a frame based CNN that was not trained on any TRECVid data, and our best short-term memorability model (0.524) tested on the Memento10k dataset, was a Bayesian Ride Regressor fit with DenseNet121 visual features.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Refereed:Yes
Subjects:Computer Science > Artificial intelligence
Computer Science > Machine learning
Computer Science > Digital video
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > INSIGHT Centre for Data Analytics
Published in: Proceedings of Mediaeval 2021. . CEUR-WS.
Publisher:CEUR-WS
Official URL:https://2021.multimediaeval.com/
Copyright Information:© 2021 The Authors
Funders:Science Foundation Ireland Grant Number SFI/12/RC/2289_P2, co-funded by the European Regional Development Fund.
ID Code:26546
Deposited On:05 Jan 2022 16:10 by Alan Smeaton . Last Modified 12 Jan 2023 16:03
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