Overview of The MediaEval 2022 Predicting Video Memorability Task
Lorin Sweeney, Mihai Gabriel Constantin, Claire-Hélène Demarty, Camilo L. Fosco, Alba García Seco de Herrera, Sebastian Halder, Graham F. Healy, Bogdan Emanuel Ionescu, Ana Matran‐Fernandez, Alan F. Smeaton, Mushfika Sultana · arXiv (Cornell University) · 2022
This paper describes the 5th edition of the 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.