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Learning Random-Walk Label Propagation for Weakly-Supervised Semantic Segmentation
CVPR 2017 | Large-scale training for semantic segmentation is challenging due to the expense of obtaining training data. Given cheaply obtained sparse image labelings, we propagate the sparse labels to produce guessed dense labelings using random-walk hitting probabilities, which leads to a differentiable parameterization with uncertainty estimates that are incorporated into our loss. We show that our method can effectively learn semantic edges given no direct edge supervision.
Collaborators: Paul Vernaza, Manmohan Chandraker
