  Salient object detection (SOD) is viewed as a pixel-wise saliency modeling
task by traditional deep learning-based methods. A limitation of current SOD
models is insufficient utilization of inter-pixel information, which usually
results in imperfect segmentation near edge regions and low spatial coherence.
As we demonstrate, using a saliency mask as the only label is suboptimal. To
address this limitation, we propose a connectivity-based approach called
bilateral connectivity network (BiconNet), which uses connectivity masks
together with saliency masks as labels for effective modeling of inter-pixel
relationships and object saliency. Moreover, we propose a bilateral voting
module to enhance the output connectivity map, and a novel edge feature
enhancement method that efficiently utilizes edge-specific features. Through
comprehensive experiments on five benchmark datasets, we demonstrate that our
proposed method can be plugged into any existing state-of-the-art
saliency-based SOD framework to improve its performance with negligible
parameter increase.
