  RGB-D salient object detection (SOD) demonstrates its superiority on
detecting in complex environments due to the additional depth information
introduced in the data. Inevitably, an independent stream is introduced to
extract features from depth images, leading to extra computation and
parameters. This methodology sacrifices the model size to improve the detection
accuracy which may impede the practical application of SOD problems. To tackle
this dilemma, we propose a dynamic distillation method along with a lightweight
structure, which significantly reduces the computational burden while
maintaining validity. This method considers the factors of both teacher and
student performance within the training stage and dynamically assigns the
distillation weight instead of applying a fixed weight on the student model. We
also investigate the issue of RGB-D early fusion strategy in distillation and
propose a simple noise elimination method to mitigate the impact of distorted
training data caused by low quality depth maps. Extensive experiments are
conducted on five public datasets to demonstrate that our method can achieve
competitive performance with a fast inference speed (136FPS) compared to 10
prior methods.
