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Temporal Action Detection in Untrimmed Videos from Fine to Coarse Granularity
2018
Applied Sciences
Temporal action detection in long, untrimmed videos is an important yet challenging task that requires not only recognizing the categories of actions in videos, but also localizing the start and end times of each action. Recent years, artificial neural networks, such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) improve the performance significantly in various computer vision tasks, including action detection. In this paper, we make the most of different granular
doi:10.3390/app8101924
fatcat:fnkew4ohkbbxdarbynmpgn6b6u