Multi-scale feature correspondence and pseudo label retraining strategy for weakly supervised semantic segmentation

Recently, the performance of semantic segmentation using weakly supervised learning has significantly improved. Weakly supervised semantic segmentation (WSSS) that uses only image-level labels has received widespread attention, it employs Class Activation Maps (CAM) to generate pseudo labels. Compared to traditional use of pixel-level labels, this technique greatly reduces annotation costs by utilizing simpler and more readily available image-level annotations. Besides, due to the local perceptual ability of Convolutional Neural Networks (CNN), the generated CAM cannot activate the entire object area. Researchers have found that this CNN limitation can be compensated for by using Vision Transformer (ViT). However, ViT also introduces an over-smoothing problem. Recent research has made good progress in solving this issue, but when discussing CAM and its related segmentation predictions, it is easy to overlook their intrinsic information and the interrelationships between them. In this paper, we propose a Multi-Scale Feature Correspondence (MSFC) method. Our MSFC can obtain the feature correspondence of CAM and segmentation predictions at different scales, re-extract useful semantic information from them, enhancing the network's learning of feature information and improving the quality of CAM. Moreover, to further improve the segmentation precision, we design a Pseudo Label Retraining Strategy (PLRS). This strategy refines the accuracy in local regions, elevates the quality of pseudo labels, and aims to enhance segmentation precision. Experimental results on the PASCAL VOC 2012 and MS COCO 2014 datasets show that our method achieves impressive performance among end-to-end WSSS methods.