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：近期，Meta AI Research 提出了一个通用的、可定制的段落任意物模型（SAM），它在一个史无前例的大型分割数据集（SA-1B）上进行预训练。毫无疑问，SAM 的出现将为各种实际图像分割应用带来巨大的益处。 在本研究中，我们对SAM在不同应用场景下的表现进行了一系列有趣的探究，特别是在自然图像、农业、制造业、遥感和医疗领域。我们分析讨论了SAM 的优缺点，并展望了分割任务未来的发展。需要说明的是，我们的工作并不打算提出新的算法或理论，而是提供了一个实践中 SAM 的全面视角。本项工作旨在为未来的通用分割研究活动提供启示。
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