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.
@article{arxiv.2212.06516,
title = {Overview of The MediaEval 2022 Predicting Video Memorability Task},
author = {Lorin Sweeney and Mihai Gabriel Constantin and Claire-Hélène Demarty and Camilo Fosco and Alba G. Seco de Herrera and Sebastian Halder and Graham Healy and Bogdan Ionescu and Ana Matran-Fernandez and Alan F. Smeaton and Mushfika Sultana},
journal= {arXiv preprint arXiv:2212.06516},
year = {2022}
}
Comments
6 pages. In: MediaEval Multimedia Benchmark Workshop Working Notes, 2022