Abstract
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.
Abstract (translated)
为了减少需要大量监督分割标注数据的必要性，已经设计了多个弱监督语义分割(WSSS)策略。这些策略通常会依赖高级数据和模型 Regularization 策略，以激发在分割先验中有用的属性(例如，预测完整度和忠实于语义边界)的发展，尽管缺乏标注信息。在本研究中，我们首先通过分析互补的 WSSS 技术和 Regularization 策略，考虑它们的优势和限制，提出了一种新的类特异性对抗 Erasing 策略，由两个对抗CAM生成网络逐步改进，以产生可靠的语义分割建议。实证结果表明，我们的策略导致基线的 effectiveness 大幅度改善，导致在Pascal VOC 2012 和 MS COCO 2014 数据集上明显改进。
URL
https://arxiv.org/abs/2305.12522
