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A Unified Variational Framework for Deep Weakly Supervised Image Segmentation
This work proposes a unified variational framework for image segmentation under sparse pixel-level supervision based on a simplex-constrained Potts model with a smooth perimeter regularizer, yielding a convex, smooth energy functional that can be used as a training loss in weakly supervised deep learning paradigms or o...