Simple Does It: Weakly Supervised Instance and Semantic Segmentation
2017pp. 1665–1674
Citations Over TimeTop 1% of 2017 papers
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 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.
Related Papers
- → Syncretic-NMS: A Merging Non-Maximum Suppression Algorithm for Instance Segmentation(2020)30 cited
- → Medical Image Segmentation with Imperfect 3D Bounding Boxes(2021)1 cited
- → Bounding-box Centralization for Improving SiamFC++(2021)1 cited
- A Method to Generate the Minimum Bounding Boxes for Shape-Arbitrary Objects(2010)