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Overview of MediaEval 2020 Predicting Media Memorability Task: What Makes a Video Memorable?

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arxiv 2012.15650 v1 pith:SZ6FIQQI submitted 2020-12-31 cs.MM cs.AIcs.CV

Alba García Seco De Herrera , Rukiye Savran Kiziltepe , Jon Chamberlain , Mihai Gabriel Constantin , Claire-Hélène Demarty , Faiyaz Doctor , Bogdan Ionescu , Alan F. Smeaton This is my paper · ORCID
classification cs.MMcs.AIcs.CV
keywords taskmemorabilitymediamediaevalpredictingvideodatasetdescription
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This paper describes the MediaEval 2020 \textit{Predicting Media Memorability} task. After first being proposed at MediaEval 2018, the Predicting Media Memorability task is in its 3rd edition this year, as the prediction of short-term and long-term video memorability (VM) remains a challenging task. In 2020, the format remained the same as in previous editions. This year the videos are a subset of the TRECVid 2019 Video-to-Text dataset, containing more action rich video content as compared with the 2019 task. In this paper a description of some aspects of this task is provided, including its main characteristics, a description of the collection, the ground truth dataset, evaluation metrics and the requirements for participants' run submissions.

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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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