  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.
