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. [] Key Method 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…
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Volume R-CNN is an end-to-end method that could perform region proposal, classification and instance segmentation all in one model, which dramatically reduces computational overhead and parameter numbers.
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This work presents a conceptually simple, flexible, and general framework for object instance segmentation that outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners.
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TLDR
It is demonstrated that the tensor view leads to large gains over baselines that ignore this structure, and leads to results comparable to Mask R-CNN, suggesting that TensorMask can serve as a foundation for novel advances in dense mask prediction and a more complete understanding of the task.
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TLDR
Experimental results verify that the proposed Mask SSD method has a comparable precision with less speed overhead as compared with state-of-the-art approaches.
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TLDR
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Multi-Task Learning via Scale Aware Feature Pyramid Networks and Effective Joint Head
  • Feng Ni, Yuehan Yao
  • Computer Science
    2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
  • 2019
TLDR
A unified head module named EJ-Head (Effective Joint Head) is proposed to combine two branches into one head, not only realizing the interaction between two tasks, but also enhancing the effectiveness of multi-task learning.
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TLDR
The method obtains competitive results compared to fully-supervised methods in certain scenarios; outperforms fully- and weakly- supervised methods with a fixed annotation budget; and is a first strong baseline for instance segmentation with point-level supervision.
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