  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 epoch-wisely creating progressive and 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.
