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dc.contributor.authorFathi, Alireza
dc.contributor.authorRehg, James M.
dc.date.accessioned2014-04-30T20:15:00Z
dc.date.available2014-04-30T20:15:00Z
dc.date.issued2013-06
dc.identifier.citationFathi, A. & Rehg, J. M. (2013). "Modeling Actions through State Changes". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013), 23-28 June 2013, pp. 2579-2586.en_US
dc.identifier.urihttp://hdl.handle.net/1853/51691
dc.description©2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en_US
dc.descriptionPresented at the 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 23-28 June 2013, Portland, OR.
dc.descriptionDOI: 10.1109/CVPR.2013.333
dc.description.abstractin this paper we present a model of action based on the change in the state of the environment. Many actions involve similar dynamics and hand-object relationships, but differ in their purpose and meaning. The key to differentiating these actions is the ability to identify how they change the state of objects and materials in the environment. We propose a weakly supervised method for learning the object and material states that are necessary for recognizing daily actions. Once these state detectors are learned, we can apply them to input videos and pool their outputs to detect actions. We further demonstrate that our method can be used to segment discrete actions from a continuous video of an activity. Our results outperform state-of-the-art action recognition and activity segmentation results.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectAction recognitionen_US
dc.subjectActivity segmentationen_US
dc.subjectState of objectsen_US
dc.subjectWeakly supervised methoden_US
dc.titleModeling Actions through State Changesen_US
dc.typePost-printen_US
dc.typeProceedingsen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Interactive Computingen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Center for Robotics and Intelligent Machinesen_US
dc.publisher.originalInstitute of Electrical and Electronics Engineers
dc.identifier.doi10.1109/CVPR.2013.333
dc.embargo.termsnullen_US


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