Learning to Predict Sequences of Human Fixations.
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The first part of the network consists of a model trained to generate saliency volumes, whose parameters are fit by back-propagation computed from a binary cross entropy (BCE) loss over downsampled versions of the saliency volumes.
We introduce PathGAN, a deep neural network for visual scanpath prediction trained on adversarial examples.
The understanding of the mechanisms behind focus of attention in a visual scene is a problem of great interest in visual perception and computer vision.
Ranked #1 on Scanpath prediction on FixaTons