Normalized Cut Loss for Weakly-supervised CNN Segmentation

CVPR 2018 Meng TangAbdelaziz DjelouahFederico PerazziYuri BoykovChristopher Schroers

Most recent semantic segmentation methods train deep convolutional neural networks with fully annotated masks requiring pixel-accuracy for good quality training. Common weakly-supervised approaches generate full masks from partial input (e.g. scribbles or seeds) using standard interactive segmentation methods as preprocessing... (read more)

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