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MediaEval 2018: Predicting Media Memorability

Authors :
Cohendet, Romain
Demarty, Claire-Hélène
Duong, Ngoc Q.K.
Sjöberg, Mats
Ionescu, Bogdan
Do, Thanh Toan
Technicolor
Professorship Kaski Samuel
University Politehnica of Bucharest
University of Adelaide
Department of Computer Science
Aalto-yliopisto
Aalto University
Publication Year :
2018
Publisher :
CEUR, 2018.

Abstract

openaire: EC/H2020/780069/EU//MeMAD 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.

Details

Language :
English
Database :
OpenAIRE
Accession number :
edsair.od.......661..fde0323bfa17c5dc28c332c520038583