  Recent deep learning based salient object detection methods which utilize
both saliency and boundary features have achieved remarkable performance.
However, most of them ignore the complementarity between saliency features and
boundary features, thus get worse predictions in scenes with low contrast
between foreground and background. To address this issue, we propose a novel
Recurrent Two-Stream Guided Refinement Network (RTGRNet) that consists of
iterating Two-Stream Guided Refinement Modules (TGRMs). TGRM consists of a
Guide Block and two feature streams: saliency and boundary, the Guide Block
utilizes the refined features after previous TGRM to further improve the
performance of two feature streams in current TGRM. Meanwhile, the low-level
integrated features are also utilized as a reference to get better details.
Finally, we progressively refine these features by recurrently stacking more
TGRMs. Extensive experiments on six public datasets show that our proposed
RTGRNet achieves the state-of-the-art performance in salient object detection.
