  For the video salient object detection (VSOD) task, how to excavate the
information from the appearance modality and the motion modality has always
been a topic of great concern. The two-stream structure, including an RGB
appearance stream and an optical flow motion stream, has been widely used as a
typical pipeline for VSOD tasks, but the existing methods usually only use
motion features to unidirectionally guide appearance features or adaptively but
blindly fuse two modality features. However, these methods underperform in
diverse scenarios due to the uncomprehensive and unspecific learning schemes.
In this paper, following a more secure modeling philosophy, we deeply
investigate the importance of appearance modality and motion modality in a more
comprehensive way and propose a VSOD network with up and down parallel
symmetry, named PSNet. Two parallel branches with different dominant modalities
are set to achieve complete video saliency decoding with the cooperation of the
Gather Diffusion Reinforcement (GDR) module and Cross-modality Refinement and
Complement (CRC) module. Finally, we use the Importance Perception Fusion (IPF)
module to fuse the features from two parallel branches according to their
different importance in different scenarios. Experiments on four dataset
benchmarks demonstrate that our method achieves desirable and competitive
performance.
