  Convolutional Neural Networks (CNN) are the state-of-the-art in the field of
visual computing. However, a major problem with CNNs is the large number of
floating point operations (FLOPs) required to perform convolutions for large
inputs. When considering the application of CNNs to video data, convolutional
filters become even more complex due to the extra temporal dimension. This
leads to problems when respective applications are to be deployed on mobile
devices, such as smart phones, tablets, micro-controllers or similar,
indicating less computational power.
  Kim et al. (2016) proposed using a Tucker-decomposition to compress the
convolutional kernel of a pre-trained network for images in order to reduce the
complexity of the network, i.e. the number of FLOPs. In this paper, we
generalize the aforementioned method for application to videos (and other 3D
signals) and evaluate the proposed method on a modified version of the THETIS
data set, which contains videos of individuals performing tennis shots. We show
that the compressed network reaches comparable accuracy, while indicating a
memory compression by a factor of 51. However, the actual computational
speed-up (factor 1.4) does not meet our theoretically derived expectation
(factor 6).
