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Found 3 papers, 3 papers with code
Gazeformer: Scalable, Effective and Fast Prediction of Goal-Directed Human Attention
In response, we pose a new task called ZeroGaze, a new variant of zero-shot learning where gaze is predicted for never-before-searched objects, and we develop a novel model, Gazeformer, to solve the ZeroGaze problem.
Unifying Top-down and Bottom-up Scanpath Prediction Using Transformers
Most models of visual attention aim at predicting either top-down or bottom-up control, as studied using different visual search and free-viewing tasks.
Target-absent Human Attention
In this paper, we propose the first data-driven computational model that addresses the search-termination problem and predicts the scanpath of search fixations made by people searching for targets that do not appear in images.
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