  Typically, a salient object detection (SOD) model faces opposite requirements
in processing object interiors and boundaries. The features of interiors should
be invariant to strong appearance change so as to pop-out the salient object as
a whole, while the features of boundaries should be selective to slight
appearance change to distinguish salient objects and background. To address
this selectivity-invariance dilemma, we propose a novel boundary-aware network
with successive dilation for image-based SOD. In this network, the feature
selectivity at boundaries is enhanced by incorporating a boundary localization
stream, while the feature invariance at interiors is guaranteed with a complex
interior perception stream. Moreover, a transition compensation stream is
adopted to amend the probable failures in transitional regions between
interiors and boundaries. In particular, an integrated successive dilation
module is proposed to enhance the feature invariance at interiors and
transitional regions. Extensive experiments on six datasets show that the
proposed approach outperforms 16 state-of-the-art methods.
