Proceedings ArticleDOI
Mask R-CNN
Kaiming He,Georgia Gkioxari,Piotr Dollár,Ross Girshick +3 more
- Vol. 35, Iss: 18, pp 2980-2988
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TLDR
This work presents a conceptually simple, flexible, and general framework for object instance segmentation, which extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition.Abstract:
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.read more
Citations
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Book ChapterDOI
Side-Aware Boundary Localization for More Precise Object Detection
Jiaqi Wang,Wenwei Zhang,Yuhang Cao,Kai Chen,Jiangmiao Pang,Tao Gong,Jianping Shi,Chen Change Loy,Dahua Lin +8 more
TL;DR: This paper proposes an alternative approach, named as Side-Aware Boundary Localization (SABL), where each side of the bounding box is respectively localized with a dedicated network branch, to tackle the difficulty of precise localization in the presence of displacements with large variance.
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References
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Proceedings ArticleDOI
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Posted Content
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: Faster R-CNN as discussed by the authors proposes a Region Proposal Network (RPN) to generate high-quality region proposals, which are used by Fast R-NN for detection.