Interpreting video features: a comparison of 3D convolutional networks and convolutional LSTM networksDownload PDF

25 Sep 2019 (modified: 24 Dec 2019)ICLR 2020 Conference Blind SubmissionReaders: Everyone
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  • Keywords: interpretability, spatiotemporal, video, features, saliency, temporal
  • TL;DR: We investigate what spatiotemporal features are focused on in video data by two models that are principally different in the way that they model temporal dependencies.
  • 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.
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