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.
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 02 Mar 2023 15:56