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dc.contributor.authorAlbanie, Samuel
dc.contributor.authorLiu, Yang
dc.contributor.authorNagrani, Arsha
dc.contributor.authorMiech, Antoine
dc.contributor.authorCoto, Ernesto
dc.contributor.authorLaptev, Ivan
dc.contributor.authorSukthankar, Rahul
dc.contributor.authorGhanem, Bernard
dc.contributor.authorZisserman, Andrew
dc.contributor.authorGabeur, Valentin
dc.contributor.authorSun, Chen
dc.contributor.authorAlahari, Karteek
dc.contributor.authorSchmid, Cordelia
dc.contributor.authorChen, Shizhe
dc.contributor.authorZhao, Yida
dc.contributor.authorJin, Qin
dc.contributor.authorCui, Kaixu
dc.contributor.authorLiu, Hui
dc.contributor.authorWang, Chen
dc.contributor.authorJiang, Yudong
dc.contributor.authorHao, Xiaoshuai
dc.date.accessioned2020-08-19T09:08:31Z
dc.date.available2020-08-19T09:08:31Z
dc.date.issued2020-08-03
dc.identifier.urihttp://hdl.handle.net/10754/664666
dc.description.abstractWe present a new video understanding pentathlon challenge, an open competition held in conjunction with the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020. The objective of the challenge was to explore and evaluate new methods for text-to-video retrieval-the task of searching for content within a corpus of videos using natural language queries. This report summarizes the results of the first edition of the challenge together with the findings of the participants.
dc.description.sponsorshipThe organisers would like to express their gratitude to the creators of the original datasets used in this challenge. They would like to thank in particular Juan Carlos Niebles, Ranjay Krishna, Luowei Zhou, Lisa Ann Hendricks, Jun Xu, Tao Mei, Ting Yao, Yong Rui, David L. Chen, Bryan Russell and Anna Rohrbach for their assistance. We gratefully acknowledge the support of the Programme Grant Seebibyte EP/M013774/1.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2008.00744
dc.rightsArchived with thanks to arXiv
dc.titleThe End-of-End-to-End: A Video Understanding Pentathlon Challenge (2020)
dc.typePreprint
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentVCC Analytics Research Group
dc.eprint.versionPre-print
dc.contributor.institutionVisual Geometry Group, Univ. of Oxford.
dc.contributor.institutionInria.
dc.contributor.institutionGoogle.
dc.contributor.institutionRenmin Univ. of China.
dc.contributor.institutionXinhua Zhiyun Tech Co. Ltd.
dc.contributor.institutionChinese Academy of Sciences.
dc.identifier.arxivid2008.00744
kaust.personGhanem, Bernard
refterms.dateFOA2020-08-19T09:09:07Z


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