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Action Recognition with Actons
2013 IEEE International Conference on Computer Vision
With the improved accessibility to an exploding amount of video data and growing demands in a wide range of video analysis applications, video-based action recognition/classification becomes an increasingly important task in computer vision. In this paper, we propose a two-layer structure for action recognition to automatically exploit a mid-level "acton" representation. The weakly-supervised actons are learned via a new max-margin multi-channel multiple instance learning framework, which candoi:10.1109/iccv.2013.442 dblp:conf/iccv/ZhuWYZT13 fatcat:otes2ngysncjldnmsfwfpe7cj4