Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation
This addresses segmentation with limited labels for computer vision applications, representing an incremental improvement over prior self-labeling methods.
The paper tackles weakly-supervised segmentation with scribble labels by proposing a soft self-labeling approach that optimizes CRF/Potts loss relaxations, which consistently improves training and outperforms more complex specialized systems, even surpassing full pixel-precise supervision in some cases.
We consider weakly supervised segmentation where only a fraction of pixels have ground truth labels (scribbles) and focus on a self-labeling approach optimizing relaxations of the standard unsupervised CRF/Potts loss on unlabeled pixels. While WSSS methods can directly optimize such losses via gradient descent, prior work suggests that higher-order optimization can improve network training by introducing hidden pseudo-labels and powerful CRF sub-problem solvers, e.g. graph cut. However, previously used hard pseudo-labels can not represent class uncertainty or errors, which motivates soft self-labeling. We derive a principled auxiliary loss and systematically evaluate standard and new CRF relaxations (convex and non-convex), neighborhood systems, and terms connecting network predictions with soft pseudo-labels. We also propose a general continuous sub-problem solver. Using only standard architectures, soft self-labeling consistently improves scribble-based training and outperforms significantly more complex specialized WSSS systems. It can outperform full pixel-precise supervision. Our general ideas apply to other weakly-supervised problems/systems.