A Survey of Lightweight Techniques for Segment Anything Model

ObjectiveThe Segment Anything Model (SAM) demonstrates strong zero-shot generalization in image segmentation and sets a new direction for visual foundation models. The original SAM, especially the ViT-Huge version with about 637 million parameters, requires high computational resources and substantial memory. This restricts deployment in resource-limited settings such as mobile devices, embedded systems, and real-time tasks. Growing demand for efficient and deployable vision models has encouraged research on lightweight variants of SAM. Existing reviews describe applications of SAM, yet a structured summary of lightweight strategies across model compression, architectural redesign, and knowledge distillation is still absent. This review addresses this need by providing a systematic analysis of current SAM lightweight research, classifying major techniques, assessing performance, and identifying challenges and future research directions for efficient visual foundation models.MethodsThis review examines recent studies on SAM lightweight methods published in leading conferences and journals. The techniques are grouped into three categories based on their technical focus. The first category, Model Compression and Acceleration, covers knowledge distillation, network pruning, and quantization. The second category, Efficient Architecture Design, replaces the ViT backbone with lightweight structures or adjusts attention mechanisms. The third category, Efficient Feature Extraction and Fusion, refines the interaction between the image encoder and prompt encoder. A comparative assessment is conducted for representative studies, considering model size, computational cost, inference speed, and segmentation accuracy on standard benchmarks (Table 3).Results and DiscussionsThe reviewed models achieve clear gains in inference speed and parameter efficiency. MobileSAM reduces the model to 9.6 M parameters, and Lite-SAM reaches up to 16× acceleration while maintaining suitable segmentation accuracy. Approaches based on knowledge distillation and hybrid design support generalization across domains such as medical imaging, video segmentation, and embedded tasks. Although accuracy and speed still show a degree of tension, the selection of a lightweight strategy depends on the intended application. Challenges remain in prompt design, multi-scale feature fusion, and deployment on low-power hardware platforms.ConclusionsThis review provides an overview of the rapidly developing field of SAM lightweight research. The development of efficient SAM models is a multifaceted challenge that requires a combination of compression, architectural innovation, and optimization strategies. Current studies show that real-time performance on edge devices can be achieved with a small reduction in accuracy. Although progress is evident, challenges remain in handling complex scenarios, reducing the cost of distillation data, and establishing unified evaluation benchmarks. Future research is expected to emphasize more generalizable lightweight architectures, explore data-free or few-shot distillation approaches, and develop standardized evaluation protocols that consider both accuracy and efficiency.