Segment anything model (SAM) developed by Meta AI Research has recently attracted significant attention. Trained on a large segmentation dataset of over 1 billion masks, SAM is capable of segmenting any object on a certain image. In the original SAM work, the authors turned to zero-short transfer tasks (like edge detection) for evaluating the performance of SAM. Recently, numerous works have attempted to investigate the performance of SAM in various scenarios to recognize and segment objects. Moreover, numerous projects have emerged to show the versatility of SAM as a foundation model by combining it with other models, like Grounding DINO, Stable Diffusion, ChatGPT, etc. With the relevant papers and projects increasing exponentially, it is challenging for the readers to catch up with the development of SAM. To this end, this work conducts the first yet comprehensive survey on SAM. This is an ongoing project and we intend to update the manuscript on a regular basis. Therefore, readers are welcome to contact us if they complete new works related to SAM so that we can include them in our next version.

翻译：Meta AI 研究团队提出的分割一切模型（SAM）近期引起了广泛关注。该模型在包含超过10亿个掩膜的大规模分割数据集上训练，能够对任意图像中的任意对象进行分割。在原始SAM工作中，作者将零样本迁移任务（如边缘检测）作为评估指标。近期大量研究开始探索SAM在不同场景下识别与分割对象的性能表现。此外，众多项目展示了SAM作为基础模型的通用性，将其与其他模型（如Grounding DINO、Stable Diffusion、ChatGPT等）相结合。随着相关论文与项目呈指数级增长，读者难以全面追踪SAM的发展脉络。为此，本文首次对SAM进行系统性综述。本工作为持续性项目，我们将定期更新文稿。欢迎读者将SAM相关新成果联系我们，以便纳入后续版本。