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ActivityNet: A large-scale video benchmark for human activity understanding
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
In spite of many dataset efforts for human action recognition, current computer vision algorithms are still severely limited in terms of the variability and complexity of the actions that they can recognize. This is in part due to the simplicity of current benchmarks, which mostly focus on simple actions and movements occurring on manually trimmed videos. In this paper we introduce ActivityNet, a new largescale video benchmark for human activity understanding. Our benchmark aims at covering a
doi:10.1109/cvpr.2015.7298698
dblp:conf/cvpr/HeilbronEGN15
fatcat:mwlxj6rbdvay7ior2fs3lb6s54