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- W3049276812 abstract "By and large, current visual attention models mostly rely, when considering static stimuli, on the following procedure. Given an image, a saliency map is computed, which, in turn, might serve the purpose of predicting a sequence of gaze shifts, namely a scanpath instantiating the dynamics of visual attention deployment. The temporal pattern of attention unfolding is thus confined to the scanpath generation stage, whilst salience is conceived as a static map, at best conflating a number of factors (bottom-up information, top-down, spatial biases, etc.). In this note we propose a novel sequential scheme that consists of a three-stage processing relying on a center-bias model, a context/layout model, and an object-based model, respectively. Each stage contributes, at different times, to the sequential sampling of the final scanpath. We compare the method against classic scanpath generation that exploits state-of-the-art static saliency model. Results show that accounting for the structure of the temporal unfolding leads to gaze dynamics close to human gaze behaviour." @default.
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- W3049276812 date "2020-01-01" @default.
- W3049276812 modified "2023-09-25" @default.
- W3049276812 title "How to Look Next? A Data-Driven Approach for Scanpath Prediction" @default.
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- W3049276812 doi "https://doi.org/10.1007/978-3-030-54994-7_10" @default.
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