Mask R-CNN

@article{He2017MaskR,
  title={Mask R-CNN},
  author={Kaiming He and Georgia Gkioxari and Piotr Doll{\'a}r and Ross B. Girshick},
  journal={2017 IEEE International Conference on Computer Vision (ICCV)},
  year={2017},
  pages={2980-2988}
}
We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps… CONTINUE READING

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