Bootstrapping the Performance of Webly Supervised Semantic Segmentation

CVPR 2018 Tong ShenGuosheng LinChunhua ShenIan Reid

Fully supervised methods for semantic segmentation require pixel-level class masks to train, the creation of which are expensive in terms of manual labour and time. In this work, we focus on weak supervision, developing a method for training a high-quality pixel-level classifier for semantic segmentation, using only image-level class labels as the provided ground-truth... (read more)

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