Scribble-based weakly-supervised semantic segmentation using sparse scribble
supervision is gaining traction as it reduces annotation costs when compared to
fully annotated alternatives. Existing methods primarily generate pseudo-labels
by diffusing labeled pixels to unlabeled ones with local cues for supervision.
However, this diffusion process fails to exploit global semantics and
class-specific cues, which are important for semantic segmentation. In this
study, we propose a class-driven scribble promotion network, which utilizes
both scribble annotations and pseudo-labels informed by image-level classes and
global semantics for supervision. Directly adopting pseudo-labels might
misguide the segmentation model, thus we design a localization rectification
module to correct foreground representations in the feature space. To further
combine the advantages of both supervisions, we also introduce a distance
entropy loss for uncertainty reduction, which adapts per-pixel confidence
weights according to the reliable region determined by the scribble and
pseudo-label's boundary. Experiments on the ScribbleSup dataset with different
qualities of scribble annotations outperform all the previous methods,
demonstrating the superiority and robustness of our method.The code is
available at
this https URL

用稀疏涂鸦监督的涂鸦增强方法进行基于弱监督语义分割，结合全局语义、类别信息和插值矫正，以及距离熵损失进行不确定性降低，实验证明该方法优于以往方法。