  Semantic segmentation is a core computer vision problem, but the high costs
of data annotation have hindered its wide application. Weakly-Supervised
Semantic Segmentation (WSSS) offers a cost-efficient workaround to extensive
labeling in comparison to fully-supervised methods by using partial or
incomplete labels. Existing WSSS methods have difficulties in learning the
boundaries of objects leading to poor segmentation results. We propose a novel
and effective framework that addresses these issues by leveraging visual
foundation models inside the bounding box. Adopting a two-stage WSSS framework,
our proposed network consists of a pseudo-label generation module and a
segmentation module. The first stage leverages Segment Anything Model (SAM) to
generate high-quality pseudo-labels. To alleviate the problem of delineating
precise boundaries, we adopt SAM inside the bounding box with the help of
another pre-trained foundation model (e.g., Grounding-DINO). Furthermore, we
eliminate the necessity of using the supervision of image labels, by employing
CLIP in classification. Then in the second stage, the generated high-quality
pseudo-labels are used to train an off-the-shelf segmenter that achieves the
state-of-the-art performance on PASCAL VOC 2012 and MS COCO 2014.
