A Transformer-Based Model for the Prediction of Human Gaze Behavior on Videos-Reference-Cited by-同舟云学术

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A Transformer-Based Model for the Prediction of Human Gaze Behavior on Videos

Published:2024-06-04 Issue: Volume: Page:1-6
ISSN:
Container-title:Proceedings of the 2024 Symposium on Eye Tracking Research and Applications
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Author:

Özdel Süleyman¹^ORCID,Rong Yao²^ORCID,Albaba Berat Mert³^ORCID,Kuo Yen-Ling⁴^ORCID,Wang Xi⁵^ORCID,Kasneci Enkelejda¹^ORCID

Affiliation:

1. Human-Centered Technologies for Learning, Technical University of Munich, Germany

2. Technical University of Munich, Germany

3. Department of Computer Science/AIT Lab, ETH Zürich, Switzerland

4. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, United States

5. Department of Computer Science, ETH, Switzerland

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3649902.3653439

Reference36 articles.

1. MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning

2. Sequence labelling and sequence classification with gaze: Novel uses of eye‐tracking data for Natural Language Processing

3. Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset

4. Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Misha Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch. 2021. Decision transformer: Reinforcement learning via sequence modeling. Advances in neural information processing systems 34 (2021), 15084–15097.

5. ImageNet: A large-scale hierarchical image database

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