To mitigate the necessity for large amounts of supervised segmentation
annotation sets, multiple Weakly Supervised Semantic Segmentation (WSSS)
strategies have been devised. These will often rely on advanced data and model
regularization strategies to instigate the development of useful properties
(e.g., prediction completeness and fidelity to semantic boundaries) in
segmentation priors, notwithstanding the lack of annotated information. In this
work, we first create a strong baseline by analyzing complementary WSSS
techniques and regularizing strategies, considering their strengths and
limitations. We then propose a new Class-specific Adversarial Erasing strategy,
comprising two adversarial CAM generating networks being gradually refined to
produce robust semantic segmentation proposals. Empirical results suggest that
our approach induces substantial improvement in the effectiveness of the
baseline, resulting in a noticeable improvement over both Pascal VOC 2012 and
MS COCO 2014 datasets.

本研究提出了一种基于类别的对抗擦除策略，在弱监督语义分割中实现模型的正则化和提高模型性能。经实验证明，该策略可以在 Pascal VOC 2012 和 MS COCO 2014 数据集上显著提高模型的语义分割效果。