title: Interpreting video features: a comparison of 3D convolutional networks and convolutional LSTM networks
abstract: A number of techniques for interpretability have been presented for deep learning
  in computer vision, typically with the goal of understanding what it is that the networks
  have actually learned underneath a given classification decision. However,
  when it comes to deep video architectures, interpretability is still in its infancy and
  we do not yet have a clear concept of how we should decode spatiotemporal features.
  In this paper, we present a study comparing how 3D convolutional networks
  and convolutional LSTM networks respectively learn features across temporally
  dependent frames. This is the first comparison of two video models that both
  convolve to learn spatial features but that have principally different methods of
  modeling time. Additionally, we extend the concept of meaningful perturbation
  introduced by Fong & Vedaldi (2017) to the temporal dimension to search for the
  most meaningful part of a sequence for a classification decision.
authors: Joonatan Mänttäri* Sofia Broomé* John Folkesson Hedvig Kjellström
hIndex: Hedvig Kjellström 31