  Recent mainstream weakly supervised semantic segmentation (WSSS) approaches
are mainly based on Class Activation Map (CAM) generated by a CNN
(Convolutional Neural Network) based image classifier. In this paper, we
propose a novel transformer-based framework, named Semantic Guided Activation
Transformer (SemFormer), for WSSS. We design a transformer-based Class-Aware
AutoEncoder (CAAE) to extract the class embeddings for the input image and
learn class semantics for all classes of the dataset. The class embeddings and
learned class semantics are then used to guide the generation of activation
maps with four losses, i.e., class-foreground, class-background, activation
suppression, and activation complementation loss. Experimental results show
that our SemFormer achieves \textbf{74.3}\% mIoU and surpasses many recent
mainstream WSSS approaches by a large margin on PASCAL VOC 2012 dataset. Code
will be available at \url{https://github.com/JLChen-C/SemFormer}.
