  Video salient object detection (VSOD) aims to locate and segment the most
attractive object by exploiting both spatial cues and temporal cues hidden in
video sequences. However, spatial and temporal cues are often unreliable in
real-world scenarios, such as low-contrast foreground, fast motion, and
multiple moving objects. To address these problems, we propose a new framework
to adaptively capture available information from spatial and temporal cues,
which contains Confidence-guided Adaptive Gate (CAG) modules and Dual
Differential Enhancement (DDE) modules. For both RGB features and optical flow
features, CAG estimates confidence scores supervised by the IoU between
predictions and the ground truths to re-calibrate the information with a gate
mechanism. DDE captures the differential feature representation to enrich the
spatial and temporal information and generate the fused features. Experimental
results on four widely used datasets demonstrate the effectiveness of the
proposed method against thirteen state-of-the-art methods.
