MediaEval 2018: Predicting Media Memorability

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationMediaEval 2018 - Multimedia Benchmark Workshop
Subtitle of host publicationWorking Notes Proceedings of the MediaEval 2018 Workshop, Sophia, Antipolis, France, 29-31 October 2018
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
EventMultimedia Benchmark Workshop - Sophia Antipolis, France
Duration: 29 Oct 201831 Oct 2018

Publication series

NameCEUR Workshop Proceedings
PublisherCEUR
Volume2283
ISSN (Print)1613-0073
ISSN (Electronic)1613-0073

Workshop

WorkshopMultimedia Benchmark Workshop
Abbreviated titleMediaEval
CountryFrance
CitySophia Antipolis
Period29/10/201831/10/2018

Researchers

  • Romain Cohendet
  • Claire-Hélène Demarty
  • Ngoc Q.K. Duong
  • Mats Sjöberg

  • Bogdan Ionescu
  • Thanh Toan Do

Research units

  • Technicolor
  • University Politehnica of Bucharest
  • University of Adelaide

Abstract

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.

ID: 31097114