Saliency Tubes: Visual Explanations for Spatio-Temporal Convolutions

Stergiou, Alexandros; Kapidis, Georgios; Kalliatakis, Grigorios; Chrysoulas, Christos; Veltkamp, Remco; Poppe, Ronald

Abstract

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 an additional dimension in order to extract features from it 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 innerworkings 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 thirdperson and egocentric action classification and enhance the set of methods and visualizations that improve 3D Convolutional Neural Networks (CNNs) intelligibility. Our code and a demo video are also available.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: 2019 IEEE International Conference on Image Processing (ICIP)
• Start Date: Sep 22, 2019
• End Date: Sep 25, 2019
• Acceptance Date: Jun 30, 2019
• Online Publication Date: Aug 26, 2019
• Publication Date: Aug 26, 2019
• Deposit Date: Feb 10, 2020
• Publisher: Institute of Electrical and Electronics Engineers
• Series ISSN: 2381-8549
• ISBN: 9781538662496
• DOI: https://doi.org/10.1109/icip.2019.8803153
• Public URL: http://researchrepository.napier.ac.uk/Output/2548328