# CenterMask: single shot instance segmentation with point representation

arXiv: https://arxiv.org/abs/2004.04446 · HTML: https://mlsanity.com/paper/2004.04446/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2020.

## Built on by (most cited first, 30 of 31)

- [Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks](https://mlsanity.com/paper/2107.07933/) (2021)
- [Audio-Visual Segmentation with Semantics](https://mlsanity.com/paper/2301.13190/) (2023)
- [CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation](https://mlsanity.com/paper/2203.01929/) (2022)
- [SOTR: Segmenting Objects with Transformers](https://mlsanity.com/paper/2108.06747/) (2021)
- [Single-Stage Keypoint-Based Category-Level Object Pose Estimation from an RGB Image](https://mlsanity.com/paper/2109.06161/) (2021)
- [SOLO: A Simple Framework for Instance Segmentation](https://mlsanity.com/paper/2106.15947/) (2021)
- [Rank & Sort Loss for Object Detection and Instance Segmentation](https://mlsanity.com/paper/2107.11669/) (2021)
- [The Devil is in the Points: Weakly Semi-Supervised Instance Segmentation via Point-Guided Mask Representation](https://mlsanity.com/paper/2303.15062/) (2023)
- [Learning Equivariant Segmentation with Instance-Unique Querying](https://mlsanity.com/paper/2210.00911/) (2022)
- [IAFA: Instance-aware Feature Aggregation for 3D Object Detection from a Single Image](https://mlsanity.com/paper/2103.03480/) (2021)
- [STC: Spatio-Temporal Contrastive Learning for Video Instance Segmentation](https://mlsanity.com/paper/2202.03747/) (2022)
- [VideoClick: Video Object Segmentation with a Single Click](https://mlsanity.com/paper/2101.06545/) (2021)
- [Real-time Instance Segmentation with Discriminative Orientation Maps](https://mlsanity.com/paper/2106.12204/) (2021)
- [Revisiting the Encoding of Satellite Image Time Series](https://mlsanity.com/paper/2305.02086/) (2023)
- [Mask-aware IoU for Anchor Assignment in Real-time Instance Segmentation](https://mlsanity.com/paper/2110.09734/) (2021)
- [Task-Specific Data Augmentation and Inference Processing for VIPriors Instance Segmentation Challenge](https://mlsanity.com/paper/2211.11282/) (2022)
- [Zoom in on the Plant: Fine-grained Analysis of Leaf, Stem and Vein Instances](https://mlsanity.com/paper/2312.08805/) (2023)
- [A Coarse-to-Fine Instance Segmentation Network with Learning Boundary Representation](https://mlsanity.com/paper/2106.10213/) (2021)
- [Instance Segmentation of Microscopic Foraminifera](https://mlsanity.com/paper/2105.14191/) (2021)
- [SE-PSNet: Silhouette-based Enhancement Feature for Panoptic Segmentation Network](https://mlsanity.com/paper/2107.05093/) (2021)
- [The Second Place Solution for ICCV2021 VIPriors Instance Segmentation Challenge](https://mlsanity.com/paper/2112.01072/) (2021)
- [Hierarchical Image Peeling: A Flexible Scale-space Filtering Framework](https://mlsanity.com/paper/2104.01534/) (2021)
- [Strong Instance Segmentation Pipeline for MMSports Challenge](https://mlsanity.com/paper/2209.13899/) (2022)
- [DPPD: Deformable Polar Polygon Object Detection](https://mlsanity.com/paper/2304.02250/) (2023)
- [Deep learning-based instance segmentation for the precise automated quantification of digital breast cancer immunohistochemistry images](https://mlsanity.com/paper/2311.13719/) (2023)
- [Uni-AIMS: AI-Powered Microscopy Image Analysis](https://mlsanity.com/paper/2505.06918/) (2025)
- [Instance segmentation with the number of clusters incorporated in embedding learning](https://mlsanity.com/paper/2103.14869/) (2021)
- [The Third Place Solution for CVPR2022 AVA Accessibility Vision and Autonomy Challenge](https://mlsanity.com/paper/2206.13718/) (2022)
- [Perceive, Excavate and Purify: A Novel Object Mining Framework for Instance Segmentation](https://mlsanity.com/paper/2304.08826/) (2023)
- [Generalized Mask-aware IoU for Anchor Assignment for Real-time Instance Segmentation](https://mlsanity.com/paper/2312.17031/) (2023)

## Abstract (arXiv)

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.