# CenterMask: single shot instance segmentation with point representation

Source: [CenterMask: single shot instance segmentation with point representation](<https://www.sota2.com/research/paper/68fa675e1e209131cea15f6d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:13:13.512Z

Authors: Yuqing Wang, Zhaoliang Xu, Hao Shen, Baoshan Cheng, Lirong Yang

Published: 2020-04-09T09:35:15.000Z

## Abstract

In this paper, we propose a single-shot instance segmentation method, which is simple, fast and accurate. There are two main challenges for one-stage instance segmentation: object instances differentiation and pixel-wise feature alignment. Accordingly, we decompose the instance segmentation into two parallel subtasks: Local Shape prediction that separates instances even in overlapping conditions, and Global Saliency generation that segments the whole image in a pixel-to-pixel manner. The outputs of the two branches are assembled to form the final instance masks. To realize that, the local shape information is adopted from the representation of object center points. Totally trained from scratch and without any bells and whistles, the proposed CenterMask achieves 34.5 mask AP with a speed of 12.3 fps, using a single-model with single-scale training/testing on the challenging COCO dataset. The accuracy is higher than all other one-stage instance segmentation methods except the 5 times slower TensorMask, which shows the effectiveness of CenterMask. Besides, our method can be easily embedded to other one-stage object detectors such as FCOS and performs well, showing the generalization of CenterMask.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Instance Segmentation on COCO (test-dev)](<https://www.sota2.com/research/sota/instance-segmentation-on-coco-test-dev.md>): Instance Segmentation; COCO (test-dev); APM: 38.1.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2004.04446>)

[PDF](<https://arxiv.org/pdf/2004.04446.pdf>)