SATSal: A Multi-Level Self-Attention Based Architecture for Visual Saliency Prediction-Reference-Cited by-同舟云学术

SATSal: A Multi-Level Self-Attention Based Architecture for Visual Saliency Prediction

Author:

Tliba Marouane¹,Kerkouri Mohamed A.¹,Ghariba Bashir²,Chetouani Aladine¹^ORCID,Coltekin Arzu³,Shehata Mohamed Sami⁴^ORCID,Bruno Alessandro⁵^ORCID

Affiliation:

1. Laboratoire PRISME, Université d’Orleans, Orleans, France

2. Department of Electrical and Computer Engineering, Faculty of Engineering, Elmergib University, Khoms, Libya

3. Institute of Interactive Technologies, University of Applied Sciences and Arts Northwestern Switzerland, Windisch, Switzerland

4. Department of Computer Science, The University of British Columbia, Kelowna, BC, Canada

5. Department of Computing and Informatics, Faculty of Science and Technology, Bournemouth University, Poole, U.K

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

Reference77 articles.

2. Very deep convolutional networks for large-scale image recognition;simonyan;arXiv 1409 1556,2015

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