Principles, applications, and advancements of the Segment Anything Model

Abstract

The Segment Anything Model (SAM) is a prominent computer vision model discussed in a review paper focusing on image segmentation. This paper explores the concepts, applications, and advancements of SAM, which excels at accurately separating diverse object types and managing visual data. It leverages convolutional neural networks (CNNs), an encoder-decoder architecture, skip connections, and spatial attention mechanism to capture fine details and contextual information across different scales. SAM finds versatile applications in various domains, including medical imaging for precise anatomical structure delineation and pathology identification. It improves recognition and classification by precise positioning and segmentation. However, the SAM model faces challenges such as complex object shapes and computational requirements for real-time deployment in resource-constrained environments. To tackle these limitations, researchers have proposed advancements like feature enhancement, network architecture modifications, and regularization techniques. Future directions may involve lightweight network designs, optimization strategies, and integration of external information to enhance accuracy, efficiency, and robustness of the SAM model.