{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mask-r-cnn","title":"Mask R-CNN","arxiv_id":"1703.06870","date":"2017-03-20","proceeding":"ICCV 2017 10","authors":["Kaiming He","Georgia Gkioxari","Piotr Dollár","Ross Girshick"],"abstract":"We present a conceptually simple, flexible, and general framework for object\ninstance segmentation. Our approach efficiently detects objects in an image\nwhile simultaneously generating a high-quality segmentation mask for each\ninstance. The method, called Mask R-CNN, extends Faster R-CNN by adding a\nbranch for predicting an object mask in parallel with the existing branch for\nbounding box recognition. Mask R-CNN is simple to train and adds only a small\noverhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to\ngeneralize to other tasks, e.g., allowing us to estimate human poses in the\nsame framework. We show top results in all three tracks of the COCO suite of\nchallenges, including instance segmentation, bounding-box object detection, and\nperson keypoint detection. Without bells and whistles, Mask R-CNN outperforms\nall existing, single-model entries on every task, including the COCO 2016\nchallenge winners. We hope our simple and effective approach will serve as a\nsolid baseline and help ease future research in instance-level recognition.\nCode has been made available at: https://github.com/facebookresearch/Detectron","url_abs":"http://arxiv.org/abs/1703.06870v3","url_pdf":"http://arxiv.org/pdf/1703.06870v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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