  We introduce ViDaS, a two-stream, fully convolutional Video, Depth-Aware
Saliency network to address the problem of attention modeling ``in-the-wild",
via saliency prediction in videos. Contrary to existing visual saliency
approaches using only RGB frames as input, our network employs also depth as an
additional modality. The network consists of two visual streams, one for the
RGB frames, and one for the depth frames. Both streams follow an
encoder-decoder approach and are fused to obtain a final saliency map. The
network is trained end-to-end and is evaluated in a variety of different
databases with eye-tracking data, containing a wide range of video content.
Although the publicly available datasets do not contain depth, we estimate it
using three different state-of-the-art methods, to enable comparisons and a
deeper insight. Our method outperforms in most cases state-of-the-art models
and our RGB-only variant, which indicates that depth can be beneficial to
accurately estimating saliency in videos displayed on a 2D screen. Depth has
been widely used to assist salient object detection problems, where it has been
proven to be very beneficial. Our problem though differs significantly from
salient object detection, since it is not restricted to specific salient
objects, but predicts human attention in a more general aspect. These two
problems not only have different objectives, but also different ground truth
data and evaluation metrics. To our best knowledge, this is the first
competitive deep learning video saliency estimation approach that combines both
RGB and Depth features to address the general problem of saliency estimation
``in-the-wild". The code will be publicly released.
