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Robust and efficient models for action recognition and localization

Dan Oneata 1 
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : Video interpretation and understanding is one of the long-term research goals in computer vision. Realistic videos such as movies present a variety of challenging machine learning problems, such as action classification/action retrieval, human tracking, human/object interaction classification, etc. Recently robust visual descriptors for video classification have been developed, and have shown that it is possible to learn visual classifiers in realistic difficult settings. However, in order to deploy visual recognition systems on large-scale in practice it becomes important to address the scalability of the techniques. The main goal is this thesis is to develop scalable methods for video content analysis (eg for ranking, or classification).
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Submitted on : Monday, October 19, 2015 - 2:52:16 PM
Last modification on : Friday, March 25, 2022 - 9:43:22 AM
Long-term archiving on: : Wednesday, January 20, 2016 - 1:00:49 PM


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  • HAL Id : tel-01217362, version 1



Dan Oneata. Robust and efficient models for action recognition and localization. Computer Vision and Pattern Recognition [cs.CV]. Université Grenoble Alpes, 2015. English. ⟨NNT : 2015GREAM019⟩. ⟨tel-01217362⟩



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