MediaEval 2018: Predicting Media Memorability

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Cohendet, Romain
dc.contributor.author Demarty, Claire-Hélène
dc.contributor.author Duong, Ngoc Q.K.
dc.contributor.author Sjöberg, Mats
dc.contributor.author Ionescu, Bogdan
dc.contributor.author Do, Thanh Toan
dc.date.accessioned 2019-01-14T09:22:24Z
dc.date.available 2019-01-14T09:22:24Z
dc.date.issued 2018
dc.identifier.citation Cohendet , R , Demarty , C-H , Duong , N Q K , Sjöberg , M , Ionescu , B & Do , T T 2018 , MediaEval 2018: Predicting Media Memorability . in MediaEval 2018 - Multimedia Benchmark Workshop : Working Notes Proceedings of the MediaEval 2018 Workshop, Sophia, Antipolis, France, 29-31 October 2018 . CEUR Workshop Proceedings , vol. 2283 , CEUR , Multimedia Benchmark Workshop , Sophia Antipolis , France , 29/10/2018 . en
dc.identifier.issn 1613-0073
dc.identifier.other PURE UUID: 742323b9-ae3c-46bb-8980-25438cbdb6c4
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/mediaeval-2018-predicting-media-memorability(742323b9-ae3c-46bb-8980-25438cbdb6c4).html
dc.identifier.other PURE LINK: http://ceur-ws.org/Vol-2283/
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/31097116/MediaEval_18_paper_1.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/35980
dc.description | openaire: EC/H2020/780069/EU//MeMAD
dc.description.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. en
dc.format.extent 3
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher CEUR
dc.relation info:eu-repo/grantAgreement/EC/H2020/780069/EU//MeMAD
dc.relation.ispartof Multimedia Benchmark Workshop en
dc.relation.ispartofseries MediaEval 2018 - Multimedia Benchmark Workshop en
dc.relation.ispartofseries CEUR Workshop Proceedings en
dc.relation.ispartofseries Volume 2283 en
dc.rights openAccess en
dc.subject.other 113 Computer and information sciences en
dc.title MediaEval 2018: Predicting Media Memorability en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department Technicolor
dc.contributor.department Professorship Kaski S.
dc.contributor.department University Politehnica of Bucharest
dc.contributor.department University of Adelaide
dc.contributor.department Department of Computer Science en
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201901141163
dc.type.version publishedVersion


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