  As moving objects always draw more attention of human eyes, the temporal
motive information is always exploited complementarily with spatial information
to detect salient objects in videos. Although efficient tools such as optical
flow have been proposed to extract temporal motive information, it often
encounters difficulties when used for saliency detection due to the movement of
camera or the partial movement of salient objects. In this paper, we
investigate the complimentary roles of spatial and temporal information and
propose a novel dynamic spatiotemporal network (DS-Net) for more effective
fusion of spatiotemporal information. We construct a symmetric two-bypass
network to explicitly extract spatial and temporal features. A dynamic weight
generator (DWG) is designed to automatically learn the reliability of
corresponding saliency branch. And a top-down cross attentive aggregation (CAA)
procedure is designed so as to facilitate dynamic complementary aggregation of
spatiotemporal features. Finally, the features are modified by spatial
attention with the guidance of coarse saliency map and then go through decoder
part for final saliency map. Experimental results on five benchmarks VOS,
DAVIS, FBMS, SegTrack-v2, and ViSal demonstrate that the proposed method
achieves superior performance than state-of-the-art algorithms. The source code
is available at https://github.com/TJUMMG/DS-Net.
