On Efficient Variants of Segment Anything Model: A Survey

2024-10-07

arXiv.org

AI Summary

While the Segment Anything Model (SAM) exhibits strong generalization capability, its substantial computational overhead hinders deployment on resource-constrained edge devices. This work presents a systematic survey of efficient SAM variants tailored for edge deployment. We introduce the first unified evaluation framework spanning diverse hardware platforms—including CPU, GPU, and Edge TPU—and conduct joint accuracy–latency–memory benchmarking on COCO and SA-1B. Our analysis categorizes acceleration techniques along six technical axes: model pruning, knowledge distillation, lightweight attention mechanisms, quantization, module substitution, and hardware-aware compilation—characterizing their Pareto-optimal trade-offs. The core contributions are: (1) an open-source, fully reproducible edge-SAM benchmark; and (2) empirical insights into the applicability domains and fundamental accuracy-efficiency trade-offs of each acceleration strategy—providing both theoretical foundations and practical guidelines for designing lightweight vision foundation models.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Diffusion Models for Vision
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Addressing high computational demands of Segment Anything Model
• Enhancing SAM efficiency for resource-limited environments
• Surveying acceleration techniques for SAM variants

Innovation

• Efficient SAM variants for resource-limited deployment
• Comprehensive review of model acceleration techniques
• Unified evaluation across hardware for performance comparison