  Human visual attention is a complex phenomenon that has been studied for
decades. Within it, the particular problem of scanpath prediction poses a
challenge, particularly due to the inter- and intra-observer variability, among
other reasons. Besides, most existing approaches to scanpath prediction have
focused on optimizing the prediction of a gaze point given the previous ones.
In this work, we present a probabilistic time-evolving approach to scanpath
prediction, based on Bayesian deep learning. We optimize our model using a
novel spatio-temporal loss function based on a combination of Kullback-Leibler
divergence and dynamic time warping, jointly considering the spatial and
temporal dimensions of scanpaths. Our scanpath prediction framework yields
results that outperform those of current state-of-the-art approaches, and are
almost on par with the human baseline, suggesting that our model is able to
generate scanpaths whose behavior closely resembles those of the real ones.
