UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation

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dc.contributor.authorLee, TaeYeopko
dc.contributor.authorLee, ByeongUkko
dc.contributor.authorShin, Inkyuko
dc.contributor.authorChoe, Jaesungko
dc.contributor.authorShin, UkCheolko
dc.contributor.authorKweon, In-Soko
dc.contributor.authorYoon, Kuk-Jinko
dc.date.accessioned2022-11-02T08:01:44Z-
dc.date.available2022-11-02T08:01:44Z-
dc.date.created2022-03-08-
dc.date.created2022-03-08-
dc.date.issued2022-06-24-
dc.identifier.citationIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022-
dc.identifier.urihttp://hdl.handle.net/10203/299285-
dc.languageEnglish-
dc.publisherComputer Vision Foundation, IEEE Computer Society-
dc.titleUDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation-
dc.typeConference-
dc.type.rimsCONF-
dc.citation.publicationnameIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022-
dc.identifier.conferencecountryUS-
dc.identifier.conferencelocationVirtual-
dc.contributor.localauthorKweon, In-So-
dc.contributor.localauthorYoon, Kuk-Jin-
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EE-Conference Papers(학술회의논문)ME-Conference Papers(학술회의논문)
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