Salient object detection enhanced pseudo-labels for weakly supervised semantic segmentation

Abstract

To address the limitations of generating the pseudo-labels based on Class Activation Maps (CAM) in the weakly supervised semantic segmentation tasks, in this paper, we propose a novel salient object fusion framework. This framework complements CAM localization information by capturing the complete contours and the edge details of salient targets through our proposed RGB-SOD network. Also, we design a saliency object selector to dynamically balance the weights of CAM and Salient Object Detection (SOD) when generating the single-class pseudo-labels, further improving the quality of the pseudo-labels. Despite its simplicity, our method achieved competitive performances of 77.52% and 77.73% on the PASCAL VOC 2012 validation and the test sets respectively, significantly enhancing the performance bottlenecks of the SOTA methods. This work highlights the importance of effectively integrating complementary information to improve weakly supervised segmentation tasks. Our source codes are publicly available at https://github.com/UGVly/SOD-For-WSSS.git.