Version 1
: Received: 25 August 2024 / Approved: 25 August 2024 / Online: 26 August 2024 (08:27:37 CEST)
How to cite:
Setu, J. H.; Islam, M.; Pasha, S. T.; Halder, N.; Hossain, E.; Mahmud, A.; Islam, A.; Alam, M. Z.; Amin, M. A. Segment Anything Model (SAM 2) Unveiled: Functionality, Applications, and Practical Implementation Across Multiple Domains. Preprints2024, 2024081790. https://doi.org/10.20944/preprints202408.1790.v1
Setu, J. H.; Islam, M.; Pasha, S. T.; Halder, N.; Hossain, E.; Mahmud, A.; Islam, A.; Alam, M. Z.; Amin, M. A. Segment Anything Model (SAM 2) Unveiled: Functionality, Applications, and Practical Implementation Across Multiple Domains. Preprints 2024, 2024081790. https://doi.org/10.20944/preprints202408.1790.v1
Setu, J. H.; Islam, M.; Pasha, S. T.; Halder, N.; Hossain, E.; Mahmud, A.; Islam, A.; Alam, M. Z.; Amin, M. A. Segment Anything Model (SAM 2) Unveiled: Functionality, Applications, and Practical Implementation Across Multiple Domains. Preprints2024, 2024081790. https://doi.org/10.20944/preprints202408.1790.v1
APA Style
Setu, J. H., Islam, M., Pasha, S. T., Halder, N., Hossain, E., Mahmud, A., Islam, A., Alam, M. Z., & Amin, M. A. (2024). Segment Anything Model (SAM 2) Unveiled: Functionality, Applications, and Practical Implementation Across Multiple Domains. Preprints. https://doi.org/10.20944/preprints202408.1790.v1
Chicago/Turabian Style
Setu, J. H., Md. Zahangir Alam and M. Ashraful Amin. 2024 "Segment Anything Model (SAM 2) Unveiled: Functionality, Applications, and Practical Implementation Across Multiple Domains" Preprints. https://doi.org/10.20944/preprints202408.1790.v1
Abstract
Segment Anything Model 2 (SAM 2) is a state-of-the-art development by Meta AI Research, designed to address the limitations of its predecessor, SAM, particularly in the realm of video segmentation. SAM 2 employs a transformer-based architecture enhanced with streaming memory, enabling real-time processing for both images and videos. This advancement is important given the exponential growth of multimedia content and the subsequent demand for efficient video analysis. Utilizing the SA-V dataset, SAM 2 excels in handling the intricate spatio-temporal dynamics inherent in video data, ensuring accurate and efficient segmentation. Key features of SAM 2 include its ability to provide real-time segmentation with minimal user interaction, maintaining robust performance even in dynamic and cluttered visual environments. This study provides a comprehensive overview of SAM 2, detailing its architecture, functionality, and diverse applications. It further explores the model's potential in improving practical implementations across various domains, emphasizing its significance in advancing real-time video analysis.
Keywords
Segment Anything Model 2; SAM 2; Video Segmentation; Real-Time Segmentation; Video Processing; Memory Attention; Streaming Memory; Interactive Segmentation; Spatio-Temporal Segmentation; Promptable Segmentation
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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