Scribble Hides Class: Promoting Scribble-Based Weakly-Supervised
  Semantic Segmentation with Its Class Label
Xinliang Zhang Lei Zhu Hangzhou He Lujia Jin Yanye Lu
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
https://github.com/Zxl19990529/Class-driven-Scribble-Promotion-Network.