A variety of recognizing architectures based on deep convolutional neural networks have been devised for labeling videos containing human motion with action labels. However, so far, most works cannot properly deal with the temporal dynamics encoded in multiple contiguous frames, which distinguishes action recognition from other recognition tasks. This paper develops a temporal extension of convolutional neural networks to exploit motion-dependent features for recognizing human action in video. Our approach differs from other recent attempts in that it uses multiplicative interactions between c...
