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.

翻译：弱监督语义分割在运用弱标签时是一个长期存在的病态问题。主流方法主要致力于提升伪标签的质量。本报告尝试探索来自强大的类别无关大型分割模型 Segment Anything 中“从提示到掩码”的潜力。具体而言，将不同弱标签作为提示输入 Segment Anything 模型，生成精确的类别掩码。这些类别掩码被用于生成伪标签，以训练分割网络。我们在 PASCAL VOC 2012 数据集上进行了广泛实验。实验结果表明，Segment Anything 可以作为优秀的伪标签生成器。代码将公开发布。