Robust Weakly Supervised Semantic Segmentation via Dual Domain Cascaded Decision and Annotation Bootstrap

Abstract

The performance of weakly supervised semantic segmentation (WSSS) can be effectively improved by introducing Web images. In order to achieve knowledge transfer using external data robustly, a robust knowledge transfer learning method of dual domain collaborative bootstrap is proposed. Firstly, the knowledge transfer from Web domain to target domain is effectively achieved through dual domain cascaded decision-making strategy, which highly promotes the robustness of WSSS. In addition, the reliability is intensively enhanced due to reducing the noise of images and improving the quality of annotation by dual domain collaborative learning. The method achieves 65.4% and 65.9% mIoU on benchmark dataset PASCAL VOC 2012 validation and test sets, respectively. The experimental results outperforming the most of WSSS methods could show the effectiveness of the proposed method.