Meta AI Research has recently released SAM (Segment Anything Model) which is trained on a large segmentation dataset of over 1 billion masks. As a foundation model in the field of computer vision, SAM (Segment Anything Model) has gained attention for its impressive performance in generic object segmentation. Despite its strong capability in a wide range of zero-shot transfer tasks, it remains unknown whether SAM can detect things in challenging setups like transparent objects. In this work, we perform an empirical evaluation of two glass-related challenging scenarios: mirror and transparent objects. We found that SAM often fails to detect the glass in both scenarios, which raises concern for deploying the SAM in safety-critical situations that have various forms of glass.

翻译：Meta AI 研究团队近期发布了基于超10亿掩码的大规模分割数据集训练的SAM（Segment Anything Model，分割一切模型）。作为计算机视觉领域的基石模型，该模型因其在通用物体分割任务中的卓越表现而备受关注。尽管SAM在广泛的零样本迁移任务中展现出强大能力，但其在透明物体等具有挑战性场景中的检测性能仍属未知。本研究对两类与玻璃相关的复杂场景——镜面与透明物体——进行了实证评估。实验结果表明，SAM在这两类场景中常无法检测玻璃，这引发了对该模型在包含多种玻璃形态的安全关键型场景中部署的担忧。