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Overview of The MediaEval 2022 Predicting Video Memorability Task

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arxiv 2212.06516 v1 pith:UQGYZ4J7 submitted 2022-12-13 cs.CV cs.AIcs.MM

Lorin Sweeney , Mihai Gabriel Constantin , Claire-Hélène Demarty , Camilo Fosco , Alba G. Seco de Herrera , Sebastian Halder , Graham Healy , Bogdan Ionescu
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Ana Matran-Fernandez Alan F. Smeaton Mushfika Sultana
This is my paper · ORCID
classification cs.CVcs.AIcs.MM
keywords datasettaskmemorabilityorderyeardatasetspredictingprediction
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 1 Pith paper

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  1. Generative Outpainting To Enhance the Memorability of Short-Form Videos

    cs.CV 2024-11 reject novelty 5.0 of 10

    Applying M3DDM and MOTIA outpainting to short videos shifts AI-predicted memorability scores, generally helping low-memorability videos and hurting high-memorability ones, but the effects are small and not statistical...

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