A Comprehensive Survey on Segment Anything Model for Vision and Beyond (2305.08196v2)

Abstract: AI is evolving towards artificial general intelligence, which refers to the ability of an AI system to perform a wide range of tasks and exhibit a level of intelligence similar to that of a human being. This is in contrast to narrow or specialized AI, which is designed to perform specific tasks with a high degree of efficiency. Therefore, it is urgent to design a general class of models, which we term foundation models, trained on broad data that can be adapted to various downstream tasks. The recently proposed segment anything model (SAM) has made significant progress in breaking the boundaries of segmentation, greatly promoting the development of foundation models for computer vision. To fully comprehend SAM, we conduct a survey study. As the first to comprehensively review the progress of segmenting anything task for vision and beyond based on the foundation model of SAM, this work focuses on its applications to various tasks and data types by discussing its historical development, recent progress, and profound impact on broad applications. We first introduce the background and terminology for foundation models including SAM, as well as state-of-the-art methods contemporaneous with SAM that are significant for segmenting anything task. Then, we analyze and summarize the advantages and limitations of SAM across various image processing applications, including software scenes, real-world scenes, and complex scenes. Importantly, many insights are drawn to guide future research to develop more versatile foundation models and improve the architecture of SAM. We also summarize massive other amazing applications of SAM in vision and beyond. Finally, we maintain a continuously updated paper list and an open-source project summary for foundation model SAM at \href{https://github.com/liliu-avril/Awesome-Segment-Anything}{\color{magenta}{here}}.

A Comprehensive Survey on Segment Anything Model for Vision and Beyond

The paper "A Comprehensive Survey on Segment Anything Model for Vision and Beyond," explores the evolution and application of the Segment Anything Model (SAM), a foundation model designed to break the boundaries of segmentation tasks in computer vision. This survey provides an extensive overview of SAM's architecture, utility across diverse data types, and its role in advancing versatile foundation models towards the ultimate goal of artificial general intelligence (AGI).

Foundation Models and SAM's Place

Foundation models have revolutionized AI by providing powerful pre-trained networks that can generalize across a variety of tasks. Recent advancements in both NLP, with models like BERT and GPT series, and CV, through innovations like ViT and CLIP, demonstrate the potential for these models to excel across domains. SAM builds on this framework for the CV community, enabling comprehensive segmentation through prompt-based tasks.

SAM Architecture and Training

SAM incorporates a powerful image encoder, prompt encoder, and mask decoder. Trained on an extensive dataset, SAM demonstrates capability in zero-shot generalization through its robust architecture. The training utilized a novel data engine to iteratively enhance dataset quality and model accuracy, culminating in the SA-1B dataset, which includes over a billion masks.

Applications Across Various Domains

The survey emphasizes SAM's application range:

• Software Scenes: SAM assists in image editing and style transfer, integrating with models like Stable Diffusion and CLIP to enhance precision and flexibility in tasks such as object filling, replacing, and unique style applications.
• Real-World Scenes: SAM proves its potential in general object detection, few-shot object counting, and moving object detection, illustrating its adaptability across a spectrum of real-world tasks.
• Complex Scenes: SAM's performance in low-contrast and thermal infrared scenes highlights its limitations and promising results. It enhances understanding of camouflaged, transparent, and thermal objects, proving useful in specialized areas like plant phenotyping.
• Medical Imaging: SAM has transformative implications for medical image segmentation, evaluated across various modalities, proving to be beneficial in automating and enhancing the precision of medical diagnostics.

Implications and Future Directions

The paper discusses SAM's broader implications, including its use in video object tracking, data annotation efficiency, and audio-visual fusion for complex tasks. It posits SAM as a critical step toward AGI by addressing observed limitations with future research tailored towards robust, versatile task-agnostic foundation models.

Conclusion

This survey provides a detailed exploration of SAM's capabilities, fortifying the foundation model landscape in computer vision. As a versatile tool, SAM paves the way for further research and development toward more robust AI systems capable of addressing diverse and complex segmentation challenges. The survey outlines potential for SAM in addressing unresolved complexities in segmentation tasks, signaling its role in the forward trajectory towards AGI.

The survey maintains an ongoing project summary to reflect the SAM model's dynamic evolution, underscoring the rapid progress in this promising field.