tSPM-Net: A probabilistic spatio-temporal approach for scanpath prediction

Predicting the path followed by the viewer's eyes when observing an image (a scanpath) is a challenging problem, particularly due to the inter- and intra-observer variability and the spatio-temporal dependencies of the visual attention process. Most existing approaches have focused on progressively optimizing the prediction of a gaze point given the previous ones. In this work we propose instead a probabilistic approach, which we call tSPM-Net. We build our method to account for observers' variability by resorting to Bayesian deep learning and a probabilistic approach. Besides, we optimize our model to jointly consider both spatial and temporal dimensions of scanpaths using a novel spatio-temporal loss function based on a combination of Kullback–Leibler divergence and dynamic time warping. Our tSPM-Net yields results that outperform those of current state-of-the-art approaches, and are closer to the human baseline, suggesting that our model is able to generate scanpaths whose behavior closely resembles those of the real ones.