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arXiv · 1807.10576

Visual Attention driven by Convolutional Features

Dario Zanca·Marco Gori
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Abstract

The understanding of where humans look in a scene is a problem of great interest in visual perception and computer vision. When eye-tracking devices are not a viable option, models of human attention can be used to predict fixations. In this paper we give two contribution. First, we show a model of visual attention that is simply based on deep convolutional neural networks trained for object classification tasks. A method for visualizing saliency maps is defined which is evaluated in a saliency prediction task. Second, we integrate the information of these maps with a bottom-up differential model of eye-movements to simulate visual attention scanpaths. Results on saliency prediction and scores of similarity with human scanpaths demonstrate the effectiveness of this model.

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Dario Zanca, Marco Gori. 2018-07-12. Visual Attention driven by Convolutional Features. https://arxiv.org/abs/1807.10576

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