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SALIENCY TUBES: VISUAL EXPLANATIONS FOR SPATIO-TEMPORAL CONVOLUTIONS

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 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.

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Paper Details

Authors:
Georgios Kapidis, Grigorios Kalliatakis, Christos Chrysoulas, Remco Veltkamp and Ronald Poppe
Submitted On:
18 September 2019 - 9:52am
Short Link:
Type:
Presentation Slides
Event:
Presenter's Name:
Alexandros Stergiou
Paper Code:
2272
Document Year:
2019
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Document Files

Saliency_Tubes_Visual_Explanations_for_Spatio-Temporal_Convolutions.pdf

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[1] Georgios Kapidis, Grigorios Kalliatakis, Christos Chrysoulas, Remco Veltkamp and Ronald Poppe, "SALIENCY TUBES: VISUAL EXPLANATIONS FOR SPATIO-TEMPORAL CONVOLUTIONS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4677. Accessed: Sep. 26, 2020.
@article{4677-19,
url = {http://sigport.org/4677},
author = {Georgios Kapidis; Grigorios Kalliatakis; Christos Chrysoulas; Remco Veltkamp and Ronald Poppe },
publisher = {IEEE SigPort},
title = {SALIENCY TUBES: VISUAL EXPLANATIONS FOR SPATIO-TEMPORAL CONVOLUTIONS},
year = {2019} }
TY - EJOUR
T1 - SALIENCY TUBES: VISUAL EXPLANATIONS FOR SPATIO-TEMPORAL CONVOLUTIONS
AU - Georgios Kapidis; Grigorios Kalliatakis; Christos Chrysoulas; Remco Veltkamp and Ronald Poppe
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4677
ER -
Georgios Kapidis, Grigorios Kalliatakis, Christos Chrysoulas, Remco Veltkamp and Ronald Poppe. (2019). SALIENCY TUBES: VISUAL EXPLANATIONS FOR SPATIO-TEMPORAL CONVOLUTIONS. IEEE SigPort. http://sigport.org/4677
Georgios Kapidis, Grigorios Kalliatakis, Christos Chrysoulas, Remco Veltkamp and Ronald Poppe, 2019. SALIENCY TUBES: VISUAL EXPLANATIONS FOR SPATIO-TEMPORAL CONVOLUTIONS. Available at: http://sigport.org/4677.
Georgios Kapidis, Grigorios Kalliatakis, Christos Chrysoulas, Remco Veltkamp and Ronald Poppe. (2019). "SALIENCY TUBES: VISUAL EXPLANATIONS FOR SPATIO-TEMPORAL CONVOLUTIONS." Web.
1. Georgios Kapidis, Grigorios Kalliatakis, Christos Chrysoulas, Remco Veltkamp and Ronald Poppe. SALIENCY TUBES: VISUAL EXPLANATIONS FOR SPATIO-TEMPORAL CONVOLUTIONS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4677