  Predicting saliency in videos is a challenging problem due to complex
modeling of interactions between spatial and temporal information, especially
when ever-changing, dynamic nature of videos is considered. Recently,
researchers have proposed large-scale datasets and models that take advantage
of deep learning as a way to understand what's important for video saliency.
These approaches, however, learn to combine spatial and temporal features in a
static manner and do not adapt themselves much to the changes in the video
content. In this paper, we introduce Gated Fusion Network for dynamic saliency
(GFSalNet), the first deep saliency model capable of making predictions in a
dynamic way via gated fusion mechanism. Moreover, our model also exploits
spatial and channel-wise attention within a multi-scale architecture that
further allows for highly accurate predictions. We evaluate the proposed
approach on a number of datasets, and our experimental analysis demonstrates
that it outperforms or is highly competitive with the state of the art.
Importantly, we show that it has a good generalization ability, and moreover,
exploits temporal information more effectively via its adaptive fusion scheme.
