Weakly-supervised image segmentation is an important task in computer vision.
A key problem is how to obtain high quality objects location from image-level
category. Classification activation mapping is a common method which can be
used to generate high-precise object location cues. However these location cues
are generally very sparse and small such that they can not provide effective
information for image segmentation. In this paper, we propose a saliency guided
image segmentation network to resolve this problem. We employ a self-attention
saliency method to generate subtle saliency maps, and render the location cues
grow as seeds by seeded region growing method to expand pixel-level labels
extent. In the process of seeds growing, we use the saliency values to weight
the similarity between pixels to control the growing. Therefore saliency
information could help generate discriminative object regions, and the effects
of wrong salient pixels can be suppressed efficiently. Experimental results on
a common segmentation dataset PASCAL VOC2012 demonstrate the effectiveness of
our method.

该研究提出了一种基于显著性引导的图像分割网络，利用自我注意力显著性方法生成微妙的显著性地图，并通过点种植区域生长方法将地点线索扩展为像素级标签，从而生成具有区分性的对象区域。实验结果表明，该方法有效降低了显著性噪声的影响，提高了弱监督图像分割的准确性。