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3DSAL: an efficient 3D-CNN architecture for video saliency prediction

Djilali, Yasser Abdelaziz Dahou, Sayah, Mohamed, McGuinness, Kevin orcid logoORCID: 0000-0003-1336-6477 and O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 (2020) 3DSAL: an efficient 3D-CNN architecture for video saliency prediction. In: VISAPP: 15th International Conference on Computer Vision Theory and Applications, 27-29 Feb 2020, Valetta, Malta. ISBN 978-989-758-402-2

Abstract
In this paper, we propose a novel 3D CNN architecture that enables us to train an effective video saliency prediction model. The model is designed to capture important motion information using multiple adjacent frames. Our model performs a cubic convolution on a set of consecutive frames to extract spatio-temporal fea- tures. This enables us to predict the saliency map for any given frame using past frames. We comprehensively investigate the performance of our model with respect to state-of-the-art video saliency models. Experimental results on three large-scale datasets, DHF1K, UCF-SPORTS and DAVIS, demonstrate the competitiveness of our approach.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Visual attention; Video saliency; Deep learning; 3D CNN
Subjects:Computer Science > Image processing
Engineering > Imaging systems
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. 4. ScitePress. ISBN 978-989-758-402-2
Publisher:ScitePress
Official URL:http://dx.doi.org/10.5220/0008875600270036
Copyright Information:© 2020 The Authors. CC BY-NC-ND 4.0
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland under Grant No. SFI/12/RC/2289P2
ID Code:24019
Deposited On:13 Dec 2019 09:57 by Noel Edward O'connor . Last Modified 05 Jan 2022 17:07
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