Simple Does It: Weakly Supervised Instance and Semantic Segmentation
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 CVPR 2017 PDF CVPR 2017 Abstract
Datasets
MS COCO
BSDS500
Results from the Paper
Ranked #1 on
Semantic Segmentation
on PASCAL VOC 2012 val
(Mean IoU metric)
