  Scribble-supervised semantic segmentation has gained much attention recently
for its promising performance without high-quality annotations. Many approaches
have been proposed. Typically, they handle this problem to either introduce a
well-labeled dataset from another related task, turn to iterative refinement
and post-processing with the graphical model, or manipulate the scribble label.
This work aims to achieve semantic segmentation supervised by scribble label
directly without auxiliary information and other intermediate manipulation.
Specifically, we impose diffusion on neural representation by random walk and
consistency on neural eigenspace by self-supervision, which forces the neural
network to produce dense and consistent predictions over the whole dataset. The
random walk embedded in the network will compute a probabilistic transition
matrix, with which the neural representation diffused to be uniform. Moreover,
given the probabilistic transition matrix, we apply the self-supervision on its
eigenspace for consistency in the image's main parts. In addition to comparing
the common scribble dataset, we also conduct experiments on the modified
datasets that randomly shrink and even drop the scribbles on image objects. The
results demonstrate the superiority of the proposed method and are even
comparable to some full-label supervised ones. The code and datasets are
available at https://github.com/panzhiyi/RW-SS.
