  Owing to the difficulties of mining spatial-temporal cues, the existing
approaches for video salient object detection (VSOD) are limited in
understanding complex and noisy scenarios, and often fail in inferring
prominent objects. To alleviate such shortcomings, we propose a simple yet
efficient architecture, termed Guidance and Teaching Network (GTNet), to
independently distil effective spatial and temporal cues with implicit guidance
and explicit teaching at feature- and decision-level, respectively. To be
specific, we (a) introduce a temporal modulator to implicitly bridge features
from motion into the appearance branch, which is capable of fusing cross-modal
features collaboratively, and (b) utilise motion-guided mask to propagate the
explicit cues during the feature aggregation. This novel learning strategy
achieves satisfactory results via decoupling the complex spatial-temporal cues
and mapping informative cues across different modalities. Extensive experiments
on three challenging benchmarks show that the proposed method can run at ~28
fps on a single TITAN Xp GPU and perform competitively against 14 cutting-edge
baselines.
