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. Moreover,
Mask R-CNN is easy to generalize to other tasks, e.g., allowing
us to estimate human poses in the same framework.
We show top results in all three tracks of the COCO suite of
challenges, including instance segmentation, bounding-box
object detection, and person keypoint detection. Without
tricks, Mask R-CNN outperforms all existing, single-model
entries on every task, including the COCO 2016 challenge
winners. We hope our simple and effective approach will
serve as a solid baseline and help ease future research in
instance-level recognition. Code will be made available.
