  Detection of salient objects in image and video is of great importance in
many computer vision applications. In spite of the fact that the state of the
art in saliency detection for still images has been changed substantially over
the last few years, there have been few improvements in video saliency
detection. This paper investigates the use of recently introduced non-local
neural networks in video salient object detection. Non-local neural networks
are applied to capture global dependencies and hence determine the salient
objects. The effect of non-local operations is studied separately on static and
dynamic saliency detection in order to exploit both appearance and motion
features. A novel deep non-local neural network architecture is introduced for
video salient object detection and tested on two well-known datasets DAVIS and
FBMS. The experimental results show that the proposed algorithm outperforms
state-of-the-art video saliency detection methods.

Computer Vision and Pattern Recognition