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Please use this identifier to cite or link to this item: https://doi.org/10.1145/3343031.3356082
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dc.titleRelation Understanding in Videos: A Grand Challenge Overview
dc.contributor.authorXindi Shang
dc.contributor.authorJunbin Xiao
dc.contributor.authorDonglin Di
dc.contributor.authorTat-Seng Chua
dc.date.accessioned2020-05-22T06:17:08Z
dc.date.available2020-05-22T06:17:08Z
dc.date.issued2019-10-21
dc.identifier.citationXindi Shang, Junbin Xiao, Donglin Di, Tat-Seng Chua (2019-10-21). Relation Understanding in Videos: A Grand Challenge Overview. ACM Multimedia 2019 : 2652 - 2656. ScholarBank@NUS Repository. https://doi.org/10.1145/3343031.3356082
dc.identifier.isbn9.78E+12
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/168419
dc.description.abstractACM Multimedia 2019 Video Relation Understanding Challenge is the first grand challenge aiming at pushing video content analysis at the relational and structural level. This year, the challenge asks the participants to explore and develop innovative algorithms to detect object entities and their relations based on a large-scale user-generated video dataset. The tasks will advance the foundation of future visual systems that are able to perform complex inferences. This paper presents an overview of the grand challenge, including background, detailed descriptions of the three proposed tasks, the corresponding datasets for training, validation and testing, and the evaluation process. © 2019 Association for Computing Machinery. ACM ISBN 978-1-4503-6889-6/19/10...$15.00
dc.publisherAssociation for Computing Machinery, Inc
dc.subjectvideo content analysis
dc.subjectvisual relation
dc.subjectobject detection
dc.subjectaction recognition
dc.subjectspatio-temporal
dc.typeConference Paper
dc.contributor.departmentDEPARTMENT OF COMPUTER SCIENCE
dc.description.doi10.1145/3343031.3356082
dc.description.sourcetitleACM Multimedia 2019
dc.description.page2652 - 2656
dc.grant.idR-252-300-002-490
dc.grant.fundingagencyInfocomm Media Development Authority
dc.grant.fundingagencyNational Research Foundation
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