Paper Info
Title
Timeception for Complex Action Recognition
Abstract
This paper focuses on the temporal aspect for recognizing human activities in videos; an important visual cue that has long been undervalued. We revisit the conventional definition of activity and restrict it to Complex Action: a set of one-actions with a weak temporal pattern that serves a specific purpose. Related works use spatiotemporal 3D convolutions with fixed kernel size, too rigid to capture the varieties in temporal extents of complex actions, and too short for long-range temporal modeling. In contrast, we use multi-scale temporal convolutions, and we reduce the complexity of 3D convolutions. The outcome is Timeception convolution layers, which reasons about minute-long temporal patterns, a factor of 8 longer than best related works. As a result, Timeception achieves impressive accuracy in recognizing the human activities of Charades, Breakfast Actions and MultiTHUMOS. Further, we demonstrate that Timeception learns long-range temporal dependencies and tolerate temporal extents of complex actions.
Year
DOI
Venue
2018
10.1109/CVPR.2019.00034
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Keywords
Field
DocType
Action Recognition,Video Analytics
Kernel (linear algebra),Pattern recognition,Convolution,Computer science,Action recognition,Temporal modeling,Artificial intelligence,restrict
Journal
Volume
ISSN
ISBN
abs/1812.01289
1063-6919
978-1-7281-3294-5
Citations 
PageRank 
References 
10
0.47
10
Authors
3
Name
Order
Citations
PageRank
Noureldien Hussein1121.50
efstratios gavves265533.41
Arnold W. M. Smeulders36000453.43