Weakly Supervised Semantic Segmentation (WSSS) with only image-level supervision has garnered increasing attention due to its low annotation cost compared to pixel-level annotation. Most existing methods rely on Class Activation Maps (CAM) to generate pixel-level pseudo labels for supervised training. However, it is well known that CAM often suffers from partial activation -- activating the most discriminative part instead of the entire object area, and false activation -- unnecessarily activating the background around the object. In this study, we introduce a simple yet effective approach to address these limitations by harnessing the recently released Segment Anything Model (SAM) to generate higher-quality pseudo labels with CAM. SAM is a segmentation foundation model that demonstrates strong zero-shot ability in partitioning images into segments but lacks semantic labels for these regions. To circumvent this, we employ pseudo labels for a specific class as the signal to select the most relevant masks and label them to generate the refined pseudo labels for this class. The segments generated by SAM are highly precise, leading to substantial improvements in partial and false activation. Moreover, existing post-processing modules for producing pseudo labels, such as AffinityNet, are often computationally heavy, with a significantly long training time. Surprisingly, we discovered that using the initial CAM with SAM can achieve on-par performance as the post-processed pseudo label generated from these modules with much less computational cost. Our approach is highly versatile and capable of seamless integration into existing WSSS models without modification to base networks or pipelines. Despite its simplicity, our approach improves the mean Intersection over Union (mIoU) of pseudo labels from five state-of-the-art WSSS methods by 6.2\% on average on the PASCAL VOC 2012 dataset.

翻译：仅依赖图像级标注的弱监督语义分割（WSSS）因标注成本远低于像素级标注而受到广泛关注。现有方法通常借助类激活图（CAM）生成用于监督训练的像素级伪标签，但CAM存在两个公认缺陷：局部激活——仅激活最具区分性的区域而非完整物体范围，以及错误激活——不必要的激活物体周边背景。本研究提出一种简洁高效的解决方案，利用近期发布的“分割一切模型”（Segment Anything Model, SAM）结合CAM生成更高质量的伪标签。SAM作为分割基础模型，在图像分割领域展现出强大的零样本能力，但缺乏对应区域的语义标签。为此，我们采用特定类别的伪标签作为信号，筛选最相关的掩码并为其标注，从而生成该类别的精细化伪标签。由于SAM生成的图像区域高度精确，有效改善了局部激活和错误激活问题。此外，现有用于生成伪标签的后处理模块（如AffinityNet）通常计算开销大，训练周期显著延长。值得注意的是，我们发现直接使用原始CAM结合SAM即可达到与这些模块生成的经后处理伪标签相当的性能，且计算成本大幅降低。本方法具有高度通用性，无需修改基础网络或管线即可无缝集成至现有WSSS模型。尽管方法简单，但在PASCAL VOC 2012数据集上，本方法将五种最先进WSSS方法的伪标签平均交并比（mIoU）提升了6.2%。