  Weakly supervised semantic segmentation with weak labels is a long-lived
ill-posed problem. Mainstream methods mainly focus on improving the quality of
pseudo labels. In this report, we attempt to explore the potential of 'prompt
to masks' from the powerful class-agnostic large segmentation model,
segment-anything. Specifically, different weak labels are used as prompts to
the segment-anything model, generating precise class masks. The class masks are
utilized to generate pseudo labels to train the segmentation networks. We have
conducted extensive experiments on PASCAL VOC 2012 dataset. Experiments
demonstrate that segment-anything can serve as a good pseudo-label generator.
The code will be made publicly available.
