# A Survey on Instance Segmentation: State of the art

arXiv: https://arxiv.org/abs/2007.00047 · HTML: https://mlsanity.com/paper/2007.00047/

## Key facts

- Cited by 85 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2020.

## Built on by (most cited first, 30 of 85)

- [Generalized Decoding for Pixel, Image, and Language](https://mlsanity.com/paper/2212.11270/) (2022)
- [RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model](https://mlsanity.com/paper/2306.16269/) (2023)
- [NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes](https://mlsanity.com/paper/2111.13260/) (2021)
- [Ovis-U1 Technical Report](https://mlsanity.com/paper/2506.23044/) (2025)
- [A Comprehensive Survey on Segment Anything Model for Vision and Beyond](https://mlsanity.com/paper/2305.08196/) (2023)
- [X-Paste: Revisiting Scalable Copy-Paste for Instance Segmentation using CLIP and StableDiffusion](https://mlsanity.com/paper/2212.03863/) (2022)
- [On Model Calibration for Long-Tailed Object Detection and Instance Segmentation](https://mlsanity.com/paper/2107.02170/) (2021)
- [SAGD: Boundary-Enhanced Segment Anything in 3D Gaussian via Gaussian Decomposition](https://mlsanity.com/paper/2401.17857/) (2024)
- [ObjectRelator: Enabling Cross-View Object Relation Understanding Across Ego-Centric and Exo-Centric Perspectives](https://mlsanity.com/paper/2411.19083/) (2024)
- [OmniTracker: Unifying Object Tracking by Tracking-with-Detection](https://mlsanity.com/paper/2303.12079/) (2023)
- [FoodSAM: Any Food Segmentation](https://mlsanity.com/paper/2308.05938/) (2023)
- [Comparing YOLOv8 and Mask R-CNN for instance segmentation in complex orchard environments](https://mlsanity.com/paper/2312.07935/) (2023)
- [Continual All-in-One Adverse Weather Removal with Knowledge Replay on a Unified Network Structure](https://mlsanity.com/paper/2403.07292/) (2024)
- [SAM2 for Image and Video Segmentation: A Comprehensive Survey](https://mlsanity.com/paper/2503.12781/) (2025)
- [MMSFormer: Multimodal Transformer for Material and Semantic Segmentation](https://mlsanity.com/paper/2309.04001/) (2023)
- [Reasoning Segmentation for Images and Videos: A Survey](https://mlsanity.com/paper/2505.18816/) (2025)
- [Dimensions of Motion: Monocular Prediction through Flow Subspaces](https://mlsanity.com/paper/2112.01502/) (2021)
- [A Simple Background Augmentation Method for Object Detection with Diffusion Model](https://mlsanity.com/paper/2408.00350/) (2024)
- [AMSnet 2.0: A Large AMS Database with AI Segmentation for Net Detection](https://mlsanity.com/paper/2505.09155/) (2025)
- [Attention mechanisms and deep learning for machine vision: A survey of the state of the art](https://mlsanity.com/paper/2106.07550/) (2021)
- [Acknowledging Focus Ambiguity in Visual Questions](https://mlsanity.com/paper/2501.02201/) (2025)
- [SSR-2D: Semantic 3D Scene Reconstruction from 2D Images](https://mlsanity.com/paper/2302.03640/) (2023)
- [A Refreshed Similarity-based Upsampler for Direct High-Ratio Feature Upsampling](https://mlsanity.com/paper/2407.02283/) (2024)
- [AIMS: All-Inclusive Multi-Level Segmentation](https://mlsanity.com/paper/2305.17768/) (2023)
- [Semi-Self-Supervised Domain Adaptation: Developing Deep Learning Models with Limited Annotated Data for Wheat Head Segmentation](https://mlsanity.com/paper/2405.07157/) (2024)
- [From Pixels to Tokens: Revisiting Object Hallucinations in Large Vision-Language Models](https://mlsanity.com/paper/2410.06795/) (2024)
- [Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development](https://mlsanity.com/paper/2411.11285/) (2024)
- [Advancing Marine Research: UWSAM Framework and UIIS10K Dataset for Precise Underwater Instance Segmentation](https://mlsanity.com/paper/2505.15581/) (2025)
- [Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology](https://mlsanity.com/paper/2106.07806/) (2021)
- [Large-batch Optimization for Dense Visual Predictions](https://mlsanity.com/paper/2210.11078/) (2022)

## Abstract (arXiv)

Object detection or localization is an incremental step in progression from coarse to fine digital image inference. It not only provides the classes of the image objects, but also provides the location of the image objects which have been classified. The location is given in the form of bounding boxes or centroids. Semantic segmentation gives fine inference by predicting labels for every pixel in the input image. Each pixel is labelled according to the object class within which it is enclosed. Furthering this evolution, instance segmentation gives different labels for separate instances of objects belonging to the same class. Hence, instance segmentation may be defined as the technique of simultaneously solving the problem of object detection as well as that of semantic segmentation. In this survey paper on instance segmentation -- its background, issues, techniques, evolution, popular datasets, related work up to the state of the art and future scope have been discussed. The paper provides valuable information for those who want to do research in the field of instance segmentation.