# MaskPlus: Improving Mask Generation for Instance Segmentation

arXiv: https://arxiv.org/abs/1907.06713 · HTML: https://mlsanity.com/paper/1907.06713/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2019.

## Built on by (most cited first, 1 of 1)

- [How can we learn (more) from challenges? A statistical approach to driving future algorithm development](https://mlsanity.com/paper/2106.09302/) (2021)

## Abstract (arXiv)

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 optimization 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.