  Weakly Supervised Semantic Segmentation (WSSS) research has explored many
directions to improve the typical pipeline CNN plus class activation maps (CAM)
plus refinements, given the image-class label as the only supervision. Though
the gap with the fully supervised methods is reduced, further abating the
spread seems unlikely within this framework. On the other hand, WSSS methods
based on Vision Transformers (ViT) have not yet explored valid alternatives to
CAM. ViT features have been shown to retain a scene layout, and object
boundaries in self-supervised learning. To confirm these findings, we prove
that the advantages of transformers in self-supervised methods are further
strengthened by Global Max Pooling (GMP), which can leverage patch features to
negotiate pixel-label probability with class probability. This work proposes a
new WSSS method dubbed ViT-PCM (ViT Patch-Class Mapping), not based on CAM. The
end-to-end presented network learns with a single optimization process, refined
shape and proper localization for segmentation masks. Our model outperforms the
state-of-the-art on baseline pseudo-masks (BPM), where we achieve $69.3\%$ mIoU
on PascalVOC 2012 $val$ set. We show that our approach has the least set of
parameters, though obtaining higher accuracy than all other approaches. In a
sentence, quantitative and qualitative results of our method reveal that
ViT-PCM is an excellent alternative to CNN-CAM based architectures.
