Boundary-preserving Mask R-CNN (ECCV 2020)
BMaskR-CNN
This code is developed on Detectron2.
Boundary-preserving Mask R-CNN
ECCV 2020
Tianheng Cheng, Xinggang Wang, Lichao Huang, Wenyu Liu
Abstract
Tremendous efforts have been made to improve mask localization accuracy in instance segmentation.
Modern instance segmentation methods relying on fully convolutional networks perform pixel-wise classification,
which ignores object boundaries and shapes, leading coarse and indistinct mask prediction results and imprecise localization.
To remedy these problems, we propose a conceptually simple yet effective Boundary-preserving Mask R-CNN (BMask R-CNN) to
leverage object boundary information to improve mask localization accuracy. BMask R-CNN contains a boundary-preserving mask
head in which object boundary and mask are mutually learned via feature fusion blocks. As a result,the mask prediction
results are better aligned with object boundaries. Without bells and whistles, BMask R-CNN outperforms Mask R-CNN by a
considerable