对比SAM3与SAM2在眼图像分割中的表现，发现SAM2更优且更快。
Eye image segmentation using visual and concept prompts with Segment Anything Model 3 (SAM3)
- 用视觉和文本提示测试SAM3在眼图分割上的表现
- SAM3在多数数据集上性能不及甚至慢于SAM2
- 适合关注医学图像分割模型选型的研究者
先前研究显示视觉基础模型在眼图像分割中具备出色的零样本性能。本文评估了最新版分割一切模型SAM3相较于SAM2在眼图像分割上的表现，并探索其新引入的文本提示模式效果。实验使用涵盖实验室高分辨率高质量视频及野外采集的TEyeD数据集等多种数据集进行评估。结果表明，在大多数情况下，无论采用视觉提示还是文本提示，SAM3的性能均未优于SAM2；且SAM2不仅表现更佳，速度也更快。因此结论为：当前仍推荐使用SAM2进行眼图像分割。此外，本文提供了可处理任意时长视频的SAM3代码库改写版本。
原文摘要 · Abstract (English)
Previous work has reported that vision foundation models show promising zero-shot performance in eye image segmentation. Here we examine whether the latest iteration of the Segment Anything Model, SAM3, offers better eye image segmentation performance than SAM2, and explore the performance of its new concept (text) prompting mode. Eye image segmentation performance was evaluated using diverse datasets encompassing both high-resolution high-quality videos from a lab environment and the TEyeD dataset consisting of challenging eye videos acquired in the wild. Results show that in most cases SAM3 with either visual or concept prompts did not perform better than SAM2, for both lab and in-the-wild datasets. Since SAM2 not only performed better but was also faster, we conclude that SAM2 remains the best option for eye image segmentation. We provide our adaptation of SAM3's codebase that allows processing videos of arbitrary duration.
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