  As Convolutional Neural Networks embed themselves into our everyday lives,
the need for them to be interpretable increases. However, there is often a
trade-off between methods that are efficient to compute but produce an
explanation that is difficult to interpret, and those that are slow to compute
but provide a more interpretable result. This is particularly challenging in
problem spaces that require a large input volume, especially video which
combines both spatial and temporal dimensions. In this work we introduce the
idea of scoring superpixels through the use of gradient based pixel scoring
techniques. We show qualitatively and quantitatively that this is able to
approximate LIME, in a fraction of the time. We investigate our techniques
using both image classification, and action recognition networks on large scale
datasets (ImageNet and Kinetics-400 respectively).
