Mask Scoring R-CNN
contains a network block to learn the quality of the predicted instance
masks. The proposed network block takes the instance feature and the
corresponding predicted mask together to regress the mask IoU. The mask
scoring strategy calibrates the misalignment between mask quality and
mask score, and improves instance segmentation performance by
prioritizing more accurate mask predictions during COCO AP evaluation.
By extensive evaluations on the COCO dataset, Mask Scoring R-CNN brings
consistent and noticeable gain with different models and different
frameworks. The network of MS R-CNN is as follows:
The left four images show good detection results with high
classification scores but low mask quality. Our method aims at solving
this problem. The rightmost image shows the case of a good mask with a
high classification score. Our method will retrain the high score. As
can be seen, scores predicted by our model can better interpret the
actual mask quality.
If you find MS R-CNN useful in your research, please consider citing:
@inproceedings{huang2019msrcnn,
author = {Zhaojin Huang and Lichao Huang and Yongchao Gong and Chang Huang and Xinggang Wang},
title = {{Mask Scoring R-CNN}},
booktitle = {CVPR},
year = {2019},
}
License
maskscoring_rcnn is released under the MIT license. See LICENSE for additional details.