  Weakly Supervised Semantic Segmentation (WSSS) is challenging, particularly
when image-level labels are used to supervise pixel level prediction. To bridge
their gap, a Class Activation Map (CAM) is usually generated to provide pixel
level pseudo labels. CAMs in Convolutional Neural Networks suffer from partial
activation ie, only the most discriminative regions are activated. Transformer
based methods, on the other hand, are highly effective at exploring global
context with long range dependency modeling, potentially alleviating the
"partial activation" issue. In this paper, we propose the first transformer
based WSSS approach, and introduce the Gradient weighted Element wise
Transformer Attention Map (GETAM). GETAM shows fine scale activation for all
feature map elements, revealing different parts of the object across
transformer layers. Further, we propose an activation aware label completion
module to generate high quality pseudo labels. Finally, we incorporate our
methods into an end to end framework for WSSS using double backward
propagation. Extensive experiments on PASCAL VOC and COCO demonstrate that our
results beat the state-of-the-art end-to-end approaches by a significant
margin, and outperform most multi-stage methods.m most multi-stage methods.
