Semi-Supervised Semantic Image Segmentation with Self-correcting Networks

CVPR 2020 Mostafa S. IbrahimArash VahdatMani RanjbarWilliam G. Macready

Building a large image dataset with high-quality object masks for semantic segmentation is costly and time consuming. In this paper, we introduce a principled semi-supervised framework that only uses a small set of fully supervised images (having semantic segmentation labels and box labels) and a set of images with only object bounding box labels (we call it the weak set)... (read more)

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