1 research outputs found
BiconNet: An Edge-preserved Connectivity-based Approach for Salient Object Detection
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