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Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment
We introduce a novel approach to Maximum A Posteriori inference based on discrete graphical models.
Learning Adaptive Regularization for Image Labeling Using Geometric Assignment
We study the inverse problem of model parameter learning for pixelwise image labeling, using the linear assignment flow and training data with ground truth.
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