Recently, Meta AI Research approaches a general, promptable Segment Anything Model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B). Without a doubt, the emergence of SAM will yield significant benefits for a wide array of practical image segmentation applications. In this study, we conduct a series of intriguing investigations into the performance of SAM across various applications, particularly in the fields of natural images, agriculture, manufacturing, remote sensing, and healthcare. We analyze and discuss the benefits and limitations of SAM and provide an outlook on future development of segmentation tasks. Note that our work does not intend to propose new algorithms or theories, but rather provide a comprehensive view of SAM in practice. This work is expected to provide insights that facilitate future research activities toward generic segmentation.

翻译：近日，Meta AI研究团队提出了一种通用的可提示分割模型SAM（Segment Anything Model），该模型在规模空前的分割数据集SA-1B上完成预训练。毋庸置疑，SAM的出现将为众多实际图像分割应用带来显著效益。本研究针对SAM在自然图像、农业、制造业、遥感及医疗健康等不同领域应用中的表现，开展了一系列富有启发性的实证研究。我们通过分析讨论SAM的优势与局限，对分割任务的未来发展进行了展望。值得注意的是，本文旨在提供SAM在实际应用中的全景式认知，而非提出新算法或新理论。期望本工作能为面向通用分割技术的后续研究提供有益启示。