评测SAM2在无类别实例分割任务中的表现，发现其对精细结构不敏感。
Evaluation Study on SAM 2 for Class-agnostic Instance-level Segmentation
- 用不同提示策略测试SAM2在三种场景下的实例分割能力
- 在高分辨率细粒度数据集DIS上表现不佳，细节分割能力弱
- 适合希望改进大视觉模型实例分割性能的研究者参考
Segment Anything Model (SAM) 在自然场景中展现出强大的零样本分割能力。新发布的 Segment Anything Model 2 (SAM2) 进一步提升了研究者对图像分割能力的期待。为评估 SAM2 在无类别实例级分割任务中的表现，我们采用不同的提示策略，针对三个相关场景进行测试：显著实例分割（SIS）、伪装实例分割（CIS）和阴影实例检测（SID）。此外，为进一步探索 SAM2 在分割细微物体结构方面的有效性，还在高分辨率的二分图像分割（DIS）基准上进行了详细测试，以评估其细粒度分割能力。定性与定量实验结果表明，SAM2 在不同场景下表现差异显著；且对高分辨率精细细节的分割不敏感。本文希望该技术报告能推动基于 SAM2 的适配器发展，旨在提升大视觉模型在无类别实例分割任务上的性能上限。
原文摘要 · Abstract (English)
Segment Anything Model (SAM) has demonstrated powerful zero-shot segmentation performance in natural scenes. The recently released Segment Anything Model 2 (SAM2) has further heightened researchers' expectations towards image segmentation capabilities. To evaluate the performance of SAM2 on class-agnostic instance-level segmentation tasks, we adopt different prompt strategies for SAM2 to cope with instance-level tasks for three relevant scenarios: Salient Instance Segmentation (SIS), Camouflaged Instance Segmentation (CIS), and Shadow Instance Detection (SID). In addition, to further explore the effectiveness of SAM2 in segmenting granular object structures, we also conduct detailed tests on the high-resolution Dichotomous Image Segmentation (DIS) benchmark to assess the fine-grained segmentation capability. Qualitative and quantitative experimental results indicate that the performance of SAM2 varies significantly across different scenarios. Besides, SAM2 is not particularly sensitive to segmenting high-resolution fine details. We hope this technique report can drive the emergence of SAM2-based adapters, aiming to enhance the performance ceiling of large vision models on class-agnostic instance segmentation tasks.
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