# Mask Encoding for Single Shot Instance Segmentation

arXiv: https://arxiv.org/abs/2003.11712 · HTML: https://mlsanity.com/paper/2003.11712/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2020.

## Built on by (most cited first, 30 of 47)

- [Towards Open Vocabulary Learning: A Survey](https://mlsanity.com/paper/2306.15880/) (2023)
- [Transformer-Based Visual Segmentation: A Survey](https://mlsanity.com/paper/2304.09854/) (2023)
- [SOLQ: Segmenting Objects by Learning Queries](https://mlsanity.com/paper/2106.02351/) (2021)
- [Sparse Instance Activation for Real-Time Instance Segmentation](https://mlsanity.com/paper/2203.12827/) (2022)
- [ISTR: End-to-End Instance Segmentation with Transformers](https://mlsanity.com/paper/2105.00637/) (2021)
- [OpenForensics: Large-Scale Challenging Dataset For Multi-Face Forgery Detection And Segmentation In-The-Wild](https://mlsanity.com/paper/2107.14480/) (2021)
- [DiffusionInst: Diffusion Model for Instance Segmentation](https://mlsanity.com/paper/2212.02773/) (2022)
- [Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation](https://mlsanity.com/paper/2104.05239/) (2021)
- [Spatial Feature Calibration and Temporal Fusion for Effective One-stage Video Instance Segmentation](https://mlsanity.com/paper/2104.05606/) (2021)
- [SOTR: Segmenting Objects with Transformers](https://mlsanity.com/paper/2108.06747/) (2021)
- [SOLO: A Simple Framework for Instance Segmentation](https://mlsanity.com/paper/2106.15947/) (2021)
- [FastInst: A Simple Query-Based Model for Real-Time Instance Segmentation](https://mlsanity.com/paper/2303.08594/) (2023)
- [BoxTeacher: Exploring High-Quality Pseudo Labels for Weakly Supervised Instance Segmentation](https://mlsanity.com/paper/2210.05174/) (2022)
- [Betrayed by Captions: Joint Caption Grounding and Generation for Open Vocabulary Instance Segmentation](https://mlsanity.com/paper/2301.00805/) (2023)
- [Multi-Scale Aligned Distillation for Low-Resolution Detection](https://mlsanity.com/paper/2109.06875/) (2021)
- [SOIT: Segmenting Objects with Instance-Aware Transformers](https://mlsanity.com/paper/2112.11037/) (2021)
- [SharpContour: A Contour-based Boundary Refinement Approach for Efficient and Accurate Instance Segmentation](https://mlsanity.com/paper/2203.13312/) (2022)
- [Eigencontours: Novel Contour Descriptors Based on Low-Rank Approximation](https://mlsanity.com/paper/2203.15259/) (2022)
- [DynaMask: Dynamic Mask Selection for Instance Segmentation](https://mlsanity.com/paper/2303.07868/) (2023)
- [CA-SSL: Class-Agnostic Semi-Supervised Learning for Detection and Segmentation](https://mlsanity.com/paper/2112.04966/) (2021)
- [You Only Learn One Query: Learning Unified Human Query for Single-Stage Multi-Person Multi-Task Human-Centric Perception](https://mlsanity.com/paper/2312.05525/) (2023)
- [VideoClick: Video Object Segmentation with a Single Click](https://mlsanity.com/paper/2101.06545/) (2021)
- [Instance As Identity: A Generic Online Paradigm for Video Instance Segmentation](https://mlsanity.com/paper/2208.03079/) (2022)
- [PatchDCT: Patch Refinement for High Quality Instance Segmentation](https://mlsanity.com/paper/2302.02693/) (2023)
- [UniNet: A Unified Scene Understanding Network and Exploring Multi-Task Relationships through the Lens of Adversarial Attacks](https://mlsanity.com/paper/2108.04584/) (2021)
- [Training-Free Dataset Pruning for Instance Segmentation](https://mlsanity.com/paper/2503.00828/) (2025)
- [Object Detection Made Simpler by Eliminating Heuristic NMS](https://mlsanity.com/paper/2101.11782/) (2021)
- [Real-time Instance Segmentation with Discriminative Orientation Maps](https://mlsanity.com/paper/2106.12204/) (2021)
- [MobileInst: Video Instance Segmentation on the Mobile](https://mlsanity.com/paper/2303.17594/) (2023)
- [CutS3D: Cutting Semantics in 3D for 2D Unsupervised Instance Segmentation](https://mlsanity.com/paper/2411.16319/) (2024)

## Abstract (arXiv)

To date, instance segmentation is dominated by twostage methods, as pioneered by Mask R-CNN. In contrast, one-stage alternatives cannot compete with Mask R-CNN in mask AP, mainly due to the difficulty of compactly representing masks, making the design of one-stage methods very challenging. In this work, we propose a simple singleshot instance segmentation framework, termed mask encoding based instance segmentation (MEInst). Instead of predicting the two-dimensional mask directly, MEInst distills it into a compact and fixed-dimensional representation vector, which allows the instance segmentation task to be incorporated into one-stage bounding-box detectors and results in a simple yet efficient instance segmentation framework. The proposed one-stage MEInst achieves 36.4% in mask AP with single-model (ResNeXt-101-FPN backbone) and single-scale testing on the MS-COCO benchmark. We show that the much simpler and flexible one-stage instance segmentation method, can also achieve competitive performance. This framework can be easily adapted for other instance-level recognition tasks. Code is available at: https://git.io/AdelaiDet