Reversible Recursive Instance-Level Object Segmentation

@article{Liang2016ReversibleRI,
  title={Reversible Recursive Instance-Level Object Segmentation},
  author={Xiaodan Liang and Yunchao Wei and Xiaohui Shen and Zequn Jie and Jiashi Feng and Liang Lin and Shuicheng Yan},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={633-641}
}
In this work, we propose a novel Reversible Recursive Instance-level Object Segmentation (R2-IOS) framework to address the challenging instance-level object segmentation task. R2-IOS consists of a reversible proposal refinement sub-network that predicts bounding box offsets for refining the object proposal locations, and an instance-level segmentation sub-network that generates the foreground mask of the dominant object instance in each proposal. By being recursive, R2-IOS iteratively optimizes… CONTINUE READING
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