  We present a simple yet effective progressive self-guided loss function to
facilitate deep learning-based salient object detection (SOD) in images. The
saliency maps produced by the most relevant works still suffer from incomplete
predictions due to the internal complexity of salient objects. Our proposed
progressive self-guided loss simulates a morphological closing operation on the
model predictions for progressively creating auxiliary training supervisions to
step-wisely guide the training process. We demonstrate that this new loss
function can guide the SOD model to highlight more complete salient objects
step-by-step and meanwhile help to uncover the spatial dependencies of the
salient object pixels in a region growing manner. Moreover, a new feature
aggregation module is proposed to capture multi-scale features and aggregate
them adaptively by a branch-wise attention mechanism. Benefiting from this
module, our SOD framework takes advantage of adaptively aggregated multi-scale
features to locate and detect salient objects effectively. Experimental results
on several benchmark datasets show that our loss function not only advances the
performance of existing SOD models without architecture modification but also
helps our proposed framework to achieve state-of-the-art performance.
