  Weakly supervised semantic segmentation (WSSS), a fundamental computer vision
task, which aims to segment out the object within only class-level labels. The
traditional methods adopt the CNN-based network and utilize the class
activation map (CAM) strategy to discover the object regions. However, such
methods only focus on the most discriminative region of the object, resulting
in incomplete segmentation. An alternative is to explore vision transformers
(ViT) to encode the image to acquire the global semantic information. Yet, the
lack of transductive bias to objects is a flaw of ViT. In this paper, we
explore the dual-augmented transformer network with self-regularization
constraints for WSSS. Specifically, we propose a dual network with both
CNN-based and transformer networks for mutually complementary learning, where
both networks augment the final output for enhancement. Massive systemic
evaluations on the challenging PASCAL VOC 2012 benchmark demonstrate the
effectiveness of our method, outperforming previous state-of-the-art methods.
