  Salient object detection is subjective in nature, which implies that multiple
estimations should be related to the same input image. Most existing salient
object detection models are deterministic following a point to point estimation
learning pipeline, making them incapable of estimating the predictive
distribution. Although latent variable model based stochastic prediction
networks exist to model the prediction variants, the latent space based on the
single clean saliency annotation is less reliable in exploring the subjective
nature of saliency, leading to less effective saliency divergence modeling.
Given multiple saliency annotations, we introduce a general divergence modeling
strategy via random sampling, and apply our strategy to an ensemble based
framework and three latent variable model based solutions to explore the
subjective nature of saliency. Experimental results prove the superior
performance of our general divergence modeling strategy.
