综述高效版分割模型，助力边缘设备部署
On Efficient Variants of Segment Anything Model: A Survey
- 梳理高效SAM变体的核心加速技术
- 对比不同方法在多种硬件上的性能表现
- 适合关注模型轻量化与落地的开发者
分割一切模型（Segment Anything Model, SAM）是图像分割任务的基础模型，具备出色的跨场景泛化能力。然而其优异性能伴随显著的计算和资源开销，难以在边缘设备等资源受限环境部署。为此，众多高效SAM变体被提出，在保持精度的同时提升效率。本综述首次全面总结这些高效变体，首先分析研究动因，接着介绍SAM核心机制与模型加速技术，随后按方法分类详述加速策略，并探讨未来研究方向。最后，我们在多种硬件平台上对代表性方法进行统一评估，基于典型基准测试其效率与准确率，清晰比较整体性能。
原文摘要 · Abstract (English)
The Segment Anything Model (SAM) is a foundational model for image segmentation tasks, known for its strong generalization across diverse applications. However, its impressive performance comes with significant computational and resource demands, making it challenging to deploy in resource-limited environments such as edge devices. To address this, a variety of SAM variants have been proposed to enhance efficiency while keeping accuracy. This survey provides the first comprehensive review of these efficient SAM variants. We begin by exploring the motivations driving this research. We then present core techniques used in SAM and model acceleration. This is followed by a detailed exploration of SAM acceleration strategies, categorized by approach, and a discussion of several future research directions. Finally, we offer a unified and extensive evaluation of these methods across various hardware, assessing their efficiency and accuracy on representative benchmarks, and providing a clear comparison of their overall performance.
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