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Simple Does It: Weakly Supervised Instance and Semantic Segmentation

2019-12-06 | |  92 |   48 |   0

Abstract

Semantic labelling and instance segmentation are two tasks that require particularly costly annotations. Starting from weak supervision in the form of bounding box detection annotations, we propose a new approach that does not require modifification of the segmentation training procedure. We show that when carefully designing the input labels from given bounding boxes, even a single round of training is enough to improve over previously reported weakly supervised results. Overall, our weak supervision approach reaches ∼95% of the quality of the fully supervised model, both for semantic labelling and instance segmentation

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