  Spatio-temporal representations in frame sequences play an important role in
the task of action recognition. Previously, a method of using optical flow as a
temporal information in combination with a set of RGB images that contain
spatial information has shown great performance enhancement in the action
recognition tasks. However, it has an expensive computational cost and requires
two-stream (RGB and optical flow) framework. In this paper, we propose MFNet
(Motion Feature Network) containing motion blocks which make it possible to
encode spatio-temporal information between adjacent frames in a unified network
that can be trained end-to-end. The motion block can be attached to any
existing CNN-based action recognition frameworks with only a small additional
cost. We evaluated our network on two of the action recognition datasets
(Jester and Something-Something) and achieved competitive performances for both
datasets by training the networks from scratch.
