A Comprehensive Survey on Segment Anything Model for Vision and Beyond

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# A Comprehensive Survey on Segment Anything Model for Vision and Beyond

> **The First Comprehensive SAM Survey: A Comprehensive Survey on Segment Anything Model for Vision and Beyond.** Chunhui Zhang, Li Liu, Yawen Cui, Guanjie Huang, Weilin Lin, Yiqian Yang, Yuehong Hu. [[paper](https://arxiv.org/abs/2305.08196)] [[homepage](https://github.com/liliu-avril/Awesome-Segment-Anything)][[中文解读](https://mp.weixin.qq.com/s/uYpRzvRp22-40x8e0pLVIg)] 

> **<p align="justify"> Abstract:** *Artificial intelligence (AI) is evolving towards artificial general intelligence, which refers to the ability of an AI system to perform a wide range of tasks and exhibit a level of intelligence similar to that of a human being. This is in contrast to narrow or specialized AI, which is designed to perform specific tasks with a high degree of efficiency. Therefore, it is urgent to design a general class of models, which we term foundation models, trained on broad data that can be adapted to various downstream tasks. The recently proposed segment anything model (SAM) has made significant progress in breaking the boundaries of segmentation, greatly promoting the development of foundation models for computer vision. To fully comprehend SAM, we conduct a survey study. As the first to comprehensively review the progress of segmenting anything task for vision and beyond based on the foundation model of SAM, this work focuses on its applications to various tasks and data types by discussing its historical development, recent progress, and profound impact on broad applications. We first introduce the background and terminology for foundation models including SAM, as well as state-of-the-art methods contemporaneous with SAM that are significant for segmenting anything task. Then, we analyze and summarize the advantages and limitations of SAM across various image processing applications, including software scenes, real-world scenes, and complex scenes. Importantly, many insights are drawn to guide future research to develop more versatile foundation models and improve the architecture of SAM. We also summarize massive other amazing applications of SAM in vision and beyond. Finally, we maintain a continuously updated paper list and an open-source project summary for foundation model SAM at [here](https://github.com/liliu-avril/Awesome-Segment-Anything).* </p>

> **Awesome Segment Anything Models:** A curated list of awesome segment anything models in computer vision and beyond. This repository supplements our survey paper. We intend to continuously update it.
#### If you like our project, please give us a star ⭐ on GitHub for latest update.

#### We strongly encourage authors of relevant works to make a pull request and add their paper's information [[here](https://docs.google.com/spreadsheets/d/1AdOc_mZrkKP7XoKL9g7YO4EEpjlxdxEDK2yOdRZ_edg/edit?usp=sharing)].


:boom:**[SAM Audio](https://github.com/facebookresearch/sam-audio): ''SAM Audio: Segment Anything in Audio'' was released.**

:boom:**[SAM 3D](https://github.com/facebookresearch/sam-3d-objects?tab=readme-ov-file#citing-sam-3d-objects): ''SAM 3D: 3Dfy Anything in Images'' was released.**

:boom:**[SAM 3](https://github.com/facebookresearch/sam3): ''SAM 3: Segment Anything with Concepts'' was released.**

:boom:**[SAM 2](https://github.com/facebookresearch/segment-anything-2): ''Segment Anything in Images and Videos'' was released.**

:boom:**[SAM](https://github.com/facebookresearch/segment-anything): ''Segment Anything'' was released.**

:boom:**[SAM & SAM2 for videos](https://github.com/983632847/SAM-for-Videos): The first survey on Segment Anything for Videos: A Systematic Survey was online.**
____


## :fire: Highlights
![Last Updated](https://badgen.net/github/last-commit/liliu-avril/Awesome-Segment-Anything?icon=github&label=last%20updated&color=green)
```
- 2025.12.15: SAM Audio was released.
- 2025.11.19: SAM 3 and SAM 3D were released.
- 2025.10.11: SAM 3 arrives! Officially announced and set to launch.
- 2025.04.22: SAM 2 won the ICLR 2025 Best Paper Honorable Mention.
- 2024.07.31: The first survey on SAM & SAM2 for Videos was online.
- 2024.07.29: The SAM 2 was released.
- 2023.07.14: "Segment Anything" was accepted by ICCV 2023 (Best Paper Honorable Mention).
- 2023.05.16: An initial version of this Awesome-Segment-Anything project.
- 2023.05.14: The first comprehensive SAM survey was online.
- 2023.04.05: The paper of "Segment Anything" was online.
```

## Cont

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