Using a region-restrictive erasing method for one-stage weakly-supervised semantic segmentation

Image segmentation is a classical and basic problem in the domain of computer vision. Due to the fact that fully supervision segmentation methods require dense time-consuming and expensive manual-annotations, lots of WSSS (Weakly-Supervised Semantic Segmentation) methods have been proposed to take advantage of the simplicity and availability of weak supervision annotations. In this work, we build an integrated framework to jointly train the classification task and the segmentation task guided by a self-supervised thinking with only image-level supervision and a compound refinement strategy. Then, we introduce a restrictive adversarial erasing approach to push our model to find more segmentation cues. We evaluate the proposed method on PASCAL VOC 2012 benchmark, and the experiments show that our method can achieve competitive performance compared with the earlier methods.