Predicting visual attention in graphic design documents

Author:

Chakraborty Souradeep1,Wei Zijun1,Kelton Conor1,Ahn Seoyoung2,Balasubramanian Aruna1,Zelinsky Gregory J.3,Samaras Dimitris1

Affiliation:

1. Computer Science, Stony Brook University, 12301 Stony Brook, New York, United States

2. Pyschology, Stony Brook University, 12301 Stony Brook, New York, United States

3. Psychology, Stony Brook University, 12301 Stony Brook, New York, United States

Funder

United States National Science Foundation

Partner University Fund

US National Science Foundation

SUNY2020 Infrastructure Transportation Security Center

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Media Technology,Signal Processing

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1. A Systematic Review of Deep Learning Approaches to Visual Saliency Prediction on Webpage Images;2024 7th International Conference on Informatics and Computational Sciences (ICICoS);2024-07-17

2. SINBAD: Saliency-informed detection of breakage caused by ad blocking;2024 IEEE Symposium on Security and Privacy (SP);2024-05-19

3. Webpage Saliency Prediction Using a Single Layer Support Vector Regressor;2024 10th International Conference on Artificial Intelligence and Robotics (QICAR);2024-02-29

4. Learning from Unique Perspectives: User-aware Saliency Modeling;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2023-06

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