Simple Does It: Weakly Supervised Instance and Semantic Segmentation

2016-03-24 · CVPR 2017 7 · Anna Khoreva, Rodrigo Benenson, Jan Hosang, Matthias Hein, Bernt Schiele

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 modification 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.

📄 PDF Abstract BibTeX arXiv:1603.07485

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