  We introduce STAViS, a spatio-temporal audiovisual saliency network that
combines spatio-temporal visual and auditory information in order to
efficiently address the problem of saliency estimation in videos. Our approach
employs a single network that combines visual saliency and auditory features
and learns to appropriately localize sound sources and to fuse the two
saliencies in order to obtain a final saliency map. The network has been
designed, trained end-to-end, and evaluated on six different databases that
contain audiovisual eye-tracking data of a large variety of videos. We compare
our method against 8 different state-of-the-art visual saliency models.
Evaluation results across databases indicate that our STAViS model outperforms
our visual only variant as well as the other state-of-the-art models in the
majority of cases. Also, the consistently good performance it achieves for all
databases indicates that it is appropriate for estimating saliency
"in-the-wild". The code is available at https://github.com/atsiami/STAViS.
