A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering

• URL: http://arxiv.org/abs/2306.06211v4
• Date: Sat, 19 Oct 2024 13:37:59 GMT
• Title: A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering
• Authors: Chaoning Zhang, Joseph Cho, Fachrina Dewi Puspitasari, Sheng Zheng, Chenghao Li, Yu Qiao, Taegoo Kang, Xinru Shan, Chenshuang Zhang, Caiyan Qin, Francois Rameau, Lik-Hang Lee, Sung-Ho Bae, Choong Seon Hong,
• Abstract summary: The Segment Anything Model (SAM), developed by Meta AI Research, offers a robust framework for image and video segmentation. This survey provides a comprehensive exploration of the SAM family, including SAM and SAM 2, highlighting their advancements in granularity and contextual understanding.
• Score: 49.732628643634975
• License:
• Abstract: The Segment Anything Model (SAM), developed by Meta AI Research, represents a significant breakthrough in computer vision, offering a robust framework for image and video segmentation. This survey provides a comprehensive exploration of the SAM family, including SAM and SAM 2, highlighting their advancements in granularity and contextual understanding. Our study demonstrates SAM's versatility across a wide range of applications while identifying areas where improvements are needed, particularly in scenarios requiring high granularity and in the absence of explicit prompts. By mapping the evolution and capabilities of SAM models, we offer insights into their strengths and limitations and suggest future research directions, including domain-specific adaptations and enhanced memory and propagation mechanisms. We believe that this survey comprehensively covers the breadth of SAM's applications and challenges, setting the stage for ongoing advancements in segmentation technology.

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