Weakly supervised semantic segmentation (WSSS) aims to bypass the need for laborious pixel-level annotation by using only image-level annotation. Most existing methods rely on Class Activation Maps (CAM) to derive pixel-level pseudo-labels and use them to train a fully supervised semantic segmentation model. Although these pseudo-labels are class-aware, indicating the coarse regions for particular classes, they are not object-aware and fail to delineate accurate object boundaries. To address this, we introduce a simple yet effective method harnessing the Segment Anything Model (SAM), a class-agnostic foundation model capable of producing fine-grained instance masks of objects, parts, and subparts. We use CAM pseudo-labels as cues to select and combine SAM masks, resulting in high-quality pseudo-labels that are both class-aware and object-aware. Our approach is highly versatile and can be easily integrated into existing WSSS methods without any modification. Despite its simplicity, our approach shows consistent gain over the state-of-the-art WSSS methods on both PASCAL VOC and MS-COCO datasets.

翻译：弱监督语义分割旨在仅使用图像级标注，从而避免繁琐的像素级标注需求。现有方法大多依赖类激活映射生成像素级伪标签，并以此训练全监督语义分割模型。尽管这些伪标签具有类别感知能力，能指示特定类别的粗略区域，但它们缺乏对象感知能力，无法勾勒出精确的对象边界。为解决这一问题，我们提出一种简单而有效的方法，利用Segment Anything Model（SAM）——一种能够生成物体、部件及子部件精细实例掩码的类别无关基础模型。我们将CAM伪标签作为线索，选择并组合SAM生成的掩码，从而得到既具备类别感知能力又具备对象感知能力的高质量伪标签。该方法具有高度通用性，可无缝集成至现有弱监督语义分割方法中而无需任何修改。尽管实现简单，我们的方法在PASCAL VOC和MS-COCO数据集上均较现有最先进弱监督语义分割方法取得了一致的性能提升。