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Hierarchical Attention Network for Action Recognition in Videos
[article]
2016
arXiv
pre-print
Understanding human actions in wild videos is an important task with a broad range of applications. In this paper we propose a novel approach named Hierarchical Attention Network (HAN), which enables to incorporate static spatial information, short-term motion information and long-term video temporal structures for complex human action understanding. Compared to recent convolutional neural network based approaches, HAN has following advantages (1) HAN can efficiently capture video temporal
arXiv:1607.06416v1
fatcat:6rfajs2f7rcidj7fwq3bypl3uq