  Deep learning approaches have been established as the main methodology for
video classification and recognition. Recently, 3-dimensional convolutions have
been used to achieve state-of-the-art performance in many challenging video
datasets. Because of the high level of complexity of these methods, as the
convolution operations are also extended to additional dimension in order to
extract features from them as well, providing a visualization for the signals
that the network interpret as informative, is a challenging task. An effective
notion of understanding the network's inner-workings would be to isolate the
spatio-temporal regions on the video that the network finds most informative.
We propose a method called Saliency Tubes which demonstrate the foremost points
and regions in both frame level and over time that are found to be the main
focus points of the network. We demonstrate our findings on widely used
datasets for third-person and egocentric action classification and enhance the
set of methods and visualizations that improve 3D Convolutional Neural Networks
(CNNs) intelligibility.
