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.

翻译：摘要：为缓解对大量监督分割标注数据集的需求，研究者已开发出多种弱监督语义分割（WSSS）策略。这些策略常依赖先进的数据与模型正则化技术，以在缺乏标注信息的情况下，促进分割先验中有效属性（例如预测完整性及语义边界保真度）的生成。本文首先通过分析互补性WSSS技术与正则化策略的优劣，构建了一个强基线模型。随后提出一种新型的类别特异性对抗擦除策略，该策略包含两个逐步优化的对抗性CAM生成网络，旨在生成鲁棒的语义分割候选区域。实验结果表明，本方法显著提升了基线模型的有效性，在Pascal VOC 2012与MS COCO 2014数据集上均取得了明显性能改进。