Compared to conventional semantic segmentation with pixel-level supervision,
Weakly Supervised Semantic Segmentation (WSSS) with image-level labels poses
the challenge that it always focuses on the most discriminative regions,
resulting in a disparity between fully supervised conditions. A typical
manifestation is the diminished precision on the object boundaries, leading to
a deteriorated accuracy of WSSS. To alleviate this issue, we propose to
adaptively partition the image content into deterministic regions (e.g.,
confident foreground and background) and uncertain regions (e.g., object
boundaries and misclassified categories) for separate processing. For uncertain
cues, we employ an activation-based masking strategy and seek to recover the
local information with self-distilled knowledge. We further assume that the
unmasked confident regions should be robust enough to preserve the global
semantics. Building upon this, we introduce a complementary self-enhancement
method that constrains the semantic consistency between these confident regions
and an augmented image with the same class labels. Extensive experiments
conducted on PASCAL VOC 2012 and MS COCO 2014 demonstrate that our proposed
single-stage approach for WSSS not only outperforms state-of-the-art benchmarks
remarkably but also surpasses multi-stage methodologies that trade complexity
for accuracy. The code can be found at
this https URL

对比传统的像素级监督语义分割，使用图像级标签的弱监督语义分割（WSSS）面临的挑战是始终专注于最具差异性的区域，导致完全监督条件下的不一致。典型的表现是在目标边界上减少了精度，从而导致 WSSS 的准确性下降。为了缓解这个问题，我们提出了一种自适应将图像内容划分为确定性区域（如自信的前景和背景）和不确定性区域（如目标边界和错误分类类别）进行分别处理的方法。对于不确定性提示，我们采用基于激活的掩膜策略，并试图通过自身提炼的知识来恢复局部信息。我们进一步假设未掩膜的自信区域应具有足够的鲁棒性以保持全局语义。基于此，我们引入了一种互补的自我增强方法，它限制了这些自信区域与具有相同类别标签的增强图片之间的语义一致性。通过在 PASCAL VOC 2012 和 MS COCO 2014 上进行的大量实验证明，我们提出的 WSSS 的单阶段方法不仅明显优于最先进的基准方法，而且在复杂性换精度的多阶段方法之上。可以在此 https URL 找到代码。