  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}}.
