  Most existing salient object detection (SOD) models are difficult to apply
due to the complex and huge model structures. Although some lightweight models
are proposed, the accuracy is barely satisfactory. In this paper, we design a
novel semantics-guided contextual fusion network (SCFNet) that focuses on the
interactive fusion of multi-level features for accurate and efficient salient
object detection. Furthermore, we apply knowledge distillation to SOD task and
provide a sizeable dataset KD-SOD80K. In detail, we transfer the rich knowledge
from a seasoned teacher to the untrained SCFNet through unlabeled images,
enabling SCFNet to learn a strong generalization ability to detect salient
objects more accurately. The knowledge distillation based SCFNet (KDSCFNet)
achieves comparable accuracy to the state-of-the-art heavyweight methods with
less than 1M parameters and 174 FPS real-time detection speed. Extensive
experiments demonstrate the robustness and effectiveness of the proposed
distillation method and SOD framework. Code and data:
https://github.com/zhangjinCV/KD-SCFNet.
