  Scribble-supervised semantic segmentation has gained much attention recently
for its promising performance without high-quality annotations. Due to the lack
of supervision, confident and consistent predictions are usually hard to
obtain. Typically, people handle these problems to either adopt an auxiliary
task with the well-labeled dataset or incorporate the graphical model with
additional requirements on scribble annotations. Instead, this work aims to
achieve semantic segmentation by scribble annotations directly without extra
information and other limitations. Specifically, we propose holistic
operations, including minimizing entropy and a network embedded random walk on
neural representation to reduce uncertainty. Given the probabilistic transition
matrix of a random walk, we further train the network with self-supervision on
its neural eigenspace to impose consistency on predictions between related
images. Comprehensive experiments and ablation studies verify the proposed
approach, which demonstrates superiority over others; it is even comparable to
some full-label supervised ones and works well when scribbles are randomly
shrunk or dropped.
