Weakly supervised semantic segmentation by iteratively refining optimal segmentation with deep cues guidance

Weakly supervised semantic segmentation under image-level label supervision has undergone impressive improvements over the past years. These approaches can significantly reduce human annotation efforts, although they remain inferior to fully supervised procedures. In this paper, we propose a novel framework that iteratively refines pixel-level annotations and optimizes segmentation network. We first produce initial deep cues using the combination of activation maps and a saliency map. To produce high-quality pixel-level annotations, a graphical model is constructed over optimal segmentation of high-quality region hierarchies to propagate information from deep cues to unmarked regions. In the training process, the initial pixel-level annotations are used as supervision to train the segmentation network and to predict segmentation masks. To correct inaccurate labels of segmentation masks, we use these segmentation masks with the graphical model to produce accurate pixel-level annotations and use them as supervision to retrain the segmentation network. Experimental results show that the proposed method can significantly outperform the weakly-supervised semantic segmentation methods using static labels. The proposed method has state-of-the-art performance, which are \(66.7\%\) mIoU score on PASCAL VOC 2012 test set and \(27.0\%\) mIoU score on MS COCO validation set.