Salient object detection (SOD) is viewed as a pixel-wise saliency modeling task by traditional deep learning-based methods. Although great progress has been made, a challenge of modern SOD models is the insufficient utilization of inter-pixel information, which usually results in imperfect segmentations near the edge regions . As we demonstrate, using a saliency map as the network output is a sub-optimal choice . To address this problem , we propose a connectivity-based approach named bilateral connectivity network (BiconNet), which uses a connectivity map instead of a saliency map as the network output 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 with negligible parameter increase. We show that our model can use any existing saliency-based SOD framework as its backbone. Through comprehensive experiments on five benchmark datasets, we demonstrate that our proposed method outperforms state-of-the-art SOD approaches .
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 .
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Although great progress has been made, a challenge of modern
A limitation of current
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segmentations near the edge regions
segmentation near edge regions and low spatial coherence
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map as the network output is a sub-optimal choice
mask as the only label is suboptimal
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problem
limitation
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named
called
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a connectivity map instead of a saliency map as the network output
connectivity masks together with saliency masks as labels
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,
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with negligible parameter increase. We show that our model can use any existing saliency-based SOD framework as its backbone.
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outperforms
can be plugged into any existing
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SOD approaches
saliency-based SOD framework to improve its performance with negligible parameter increase