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 \href{https://github.com/liliu-avril/Awesome-Segment-Anything}{\color{magenta}{here}}.

翻译：人工智能正朝着通用人工智能的方向演进，这指的是人工智能系统能够执行多种任务，并展现出与人类相仿的智能水平。这与旨在高效完成特定任务的狭义或专项人工智能形成对比。因此，设计一类通用的模型（我们称之为基础模型）变得迫在眉睫，这类模型在广泛的数据上进行训练，并能适应各种下游任务。最近提出的“分割一切模型”（SAM）在打破分割边界方面取得了显著进展，极大地推动了计算机视觉基础模型的发展。为了全面理解SAM，我们开展了一项综述研究。作为首个基于SAM这一基础模型，全面回顾视觉及其他领域中“分割一切”任务进展的工作，本文聚焦于SAM在不同任务和数据类型的应用，讨论了其历史发展、最新进展以及对广泛应用的深远影响。我们首先介绍了包括SAM在内的基础模型的背景和术语，以及与SAM同期出现的、对“分割一切”任务具有重要意义的先进方法。接着，我们分析并总结了SAM在各类图像处理应用中的优势与局限，包括软件场景、真实场景和复杂场景。重要的是，我们提炼了许多见解，以指导未来研究开发更通用的基础模型并改进SAM的架构。我们还总结了SAM在视觉及其他领域的众多其他惊人应用。最后，我们在\href{https://github.com/liliu-avril/Awesome-Segment-Anything}{\color{magenta}{此链接}}维护了一份持续更新的论文列表和基础模型SAM的开源项目总结。