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 Research 开发的分割一切模型（SAM）近期引起了广泛关注。该模型在包含超过十亿个掩码的大规模分割数据集上训练，能够对图像中的任意物体进行分割。在原始 SAM 工作中，作者转向零样本迁移任务（如边缘检测）来评估 SAM 的性能。近期，大量研究尝试探究 SAM 在多种场景下识别和分割物体的表现。此外，众多项目涌现，通过将 SAM 与其他模型（如 Grounding DINO、Stable Diffusion、ChatGPT 等）结合，展示了其作为基础模型的通用性。随着相关论文和项目呈指数级增长，读者难以跟上 SAM 的发展步伐。为此，本文首次对 SAM 进行了全面综述。这是一个持续更新的项目，并计划定期更新稿件。因此，欢迎读者联系我们提交与 SAM 相关的新成果，以便纳入下一版本。