MaskPlus: Improving Mask Generation for Instance Segmentation

Shichao Xu, Shuyue Lan, Zhu Qi; The IEEE Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 2030-2038

Abstract


Instance segmentation is a promising yet challenging topic in computer vision. Recent approaches such as Mask R-CNN typically divide this problem into two parts -- a detection component and a mask generation branch, and mostly focus on the improvement of the detection part. In this paper, we present an approach that extends Mask R-CNN with five novel techniques for improving the mask generation branch and reducing the conflicts between the mask branch and the detection component in training. These five techniques are independent to each other and can be flexibly utilized in building various instance segmentation architectures for increasing the overall accuracy. We demonstrate the effectiveness of our approach with tests on the COCO dataset.

Related Material


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[bibtex]
@InProceedings{Xu_2020_WACV,
author = {Xu, Shichao and Lan, Shuyue and Qi, Zhu},
title = {MaskPlus: Improving Mask Generation for Instance Segmentation},
booktitle = {The IEEE Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2020}
}