MediaEval 2018: Predicting Media Memorability

Romain Cohendet, Claire Hélène Demarty, Ngoc Q.K. Duong, Mats Sjöberg, Bogdan Ionescu, Thanh-Toan Do

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

4 Citations (Scopus)


In this paper, we present the Predicting Media Memorability task, which is proposed as part of the MediaEval 2018 Benchmarking Initiative for Multimedia Evaluation. Participants are expected to design systems that automatically predict memorability scores for videos, which reflect the probability of a video being remembered. In contrast to previous work in image memorability prediction, where memorability was measured a few minutes after memorization, the proposed dataset comes with "short-term" and "long-term" memorability annotations. All task characteristics are described, namely: the task's challenges and breakthrough, the released data set and ground truth, the required runs and the evaluation metrics. Copyright held by the owner/author(s).

Original languageEnglish
Title of host publicationWorking Notes Proceedings of the MediaEval 2018 Workshop
EditorsMartha Larson, Piyush Arora, Claire-Hélène Demarty, Michael Riegler, Benjamin Bischke, Emmanuel Dellandrea, Mathias Lux, Alastair Porter, Gareth J.F. Jones
Place of PublicationAachen Germany
Number of pages3
Publication statusPublished - 2018
Externally publishedYes
EventWorking Notes Proceedings of the MediaEval Workshop 2018 - Sophia Antipolis, France
Duration: 29 Oct 201831 Oct 2018 (Proceedings)

Publication series

NameCEUR Workshop Proceedings
PublisherCEUW - WS
ISSN (Print)1613-0073


ConferenceWorking Notes Proceedings of the MediaEval Workshop 2018
Abbreviated titleMediaEval 2018
CitySophia Antipolis
Internet address

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