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dc.contributor.authorIkizler-Cinbis, Nazlien_US
dc.contributor.authorCinbis, Gokberken_US
dc.contributor.authorSclaroff, Stanen_US
dc.date.accessioned2012-05-21T18:59:36Z
dc.date.available2012-05-21T18:59:36Z
dc.date.issued2010-07-06en_US
dc.identifier.citationIkizler-Cinbis, Nazli; Cinbis, Gokberk; Sclaroff, Stan. "Learning Actions From the Web", Technical Report BUCS-TR-2010-017, Computer Science Department, Boston University, July 6, 2010. [Available from: http://hdl.handle.net/2144/3794]en_US
dc.identifier.urihttp://hdl.handle.net/2144/3794
dc.description.abstractThis paper proposes a generic method for action recognition in uncontrolled videos. The idea is to use images collected from the Web to learn representations of actions and use this knowledge to automatically annotate actions in videos. Our approach is unsupervised in the sense that it requires no human intervention other than the text querying. Its benefits are two-fold: 1) we can improve retrieval of action images, and 2) we can collect a large generic database of action poses, which can then be used in tagging videos. We present experimental evidence that using action images collected from the Web, annotating actions is possible.en_US
dc.language.isoen-USen_US
dc.publisherCS Department, Boston Universityen_US
dc.relation.ispartofseriesBUCS Technical Reports;BUCS-TR-2010-017en_US
dc.titleLearning Actions From the Weben_US
dc.typeTechnical Reporten_US


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