  Salient object detection has achieved great improvement by using the Fully
Convolution Network (FCN). However, the FCN-based U-shape architecture may
cause the dilution problem in the high-level semantic information during the
up-sample operations in the top-down pathway. Thus, it can weaken the ability
of salient object localization and produce degraded boundaries. To this end, in
order to overcome this limitation, we propose a novel pyramid self-attention
module (PSAM) and the adoption of an independent feature-complementing
strategy. In PSAM, self-attention layers are equipped after multi-scale pyramid
features to capture richer high-level features and bring larger receptive
fields to the model. In addition, a channel-wise attention module is also
employed to reduce the redundant features of the FPN and provide refined
results. Experimental analysis shows that the proposed PSAM effectively
contributes to the whole model so that it outperforms state-of-the-art results
over five challenging datasets. Finally, quantitative results show that PSAM
generates clear and integral salient maps which can provide further help to
other computer vision tasks, such as object detection and semantic
segmentation.
