  Video salient object detection (VSOD) is an important task in many vision
applications. Reliable VSOD requires to simultaneously exploit the information
from both the spatial domain and the temporal domain. Most of the existing
algorithms merely utilize simple fusion strategies, such as addition and
concatenation, to merge the information from different domains. Despite their
simplicity, such fusion strategies may introduce feature redundancy, and also
fail to fully exploit the relationship between multi-level features extracted
from both spatial and temporal domains. In this paper, we suggest an adaptive
local-global refinement framework for VSOD. Different from previous approaches,
we propose a local refinement architecture and a global one to refine the
simply fused features with different scopes, which can fully explore the local
dependence and the global dependence of multi-level features. In addition, to
emphasize the effective information and suppress the useless one, an adaptive
weighting mechanism is designed based on graph convolutional neural network
(GCN). We show that our weighting methodology can further exploit the feature
correlations, thus driving the network to learn more discriminative feature
representation. Extensive experimental results on public video datasets
demonstrate the superiority of our method over the existing ones.
