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Scribble-supervised semantic segmentation has gained much attention recentlyfor its promising performance without high-quality annotations. Due to the lackof supervision, confident and consistent predictions are usually hard toobtain. Typically, people handle these problems to either adopt an auxiliarytask with the well-labeled dataset or incorporate the graphical model withadditional requirements on scribble annotations. Instead, this work aims toachieve semantic segmentation by scribble annotations directly without extrainformation and other limitations. Specifically, we propose holisticoperations, including minimizing entropy and a network embedded random walk onneural representation to reduce uncertainty. Given the probabilistic transitionmatrix of a random walk, we further train the network with self-supervision onits neural eigenspace to impose consistency on predictions between relatedimages. Comprehensive experiments and ablation studies verify the proposedapproach, which demonstrates superiority over others; it is even comparable tosome full-label supervised ones and works well when scribbles are randomlyshrunk or dropped.

Scribble-Supervised Semantic Segmentation by Uncertainty Reduction on Neural Representation and Self-Supervision on Neural Eigenspace