Weakly-Supervised Semantic Segmentation With Regional Location Cutting and Dynamic Credible Regions Correction

Weakly-supervised semantic segmentation is a challenging task as it outputs pixel-level predictions from weaker labels. Segmentation with weaker labels is an important research area since it can significantly reduce human annotation efforts by associating high-level semantic to low-level appearance. In this article, we propose a novel Regional location Cutting and Dynamic credible regions Correction (RCDC) approach for weakly-supervised semantic segmentation. Only image-level labels are needed and it can take less time for manual annotation. Starting with the weak localization of classification network, our cutting approach combines the weak coverage with the traditional cutting method to obtain the pseudo-labels of around 50% ground truth. Then, our dynamic credible regions correction approach adjusts the loss function during the training to preserve the regions that have the superior performance of each iteration. It can further enhance the pseudo-labels for better segmentation results. Finally, with the fully-connected CRF and the retraining method, our approach obtains a competitive performance on the PASCAL VOC 2012 dataset.