# A Comprehensive Survey on Segment Anything Model for Vision and Beyond

**Authors:** Chunhui Zhang, Li Liu, Yawen Cui, Guanjie Huang, Weilin Lin, Yiqian Yang, Yuehong Hu  
**arXiv:** [2305.08196](https://arxiv.org/abs/2305.08196)  
**Date:** 2023-05-14  
**Summary page:** https://www.summarizepaper.com/en/paper/2305.08196v2/

## Key points

- Artificial intelligence (AI) is progressing towards artificial general intelligence to mimic human-level intelligence across various tasks
- The Segment Anything Model (SAM) is a crucial foundation model that has made significant progress in segmentation tasks within computer vision
- The Extended Anchor Concept (EAC) utilizes SAM for providing explanations for deep neural network predictions on input images
- Concerns exist about potential negative social impacts if EAC is misapplied in sensitive domains, leading to misleading explanations with severe consequences
- SAM's historical development, terminology, applications, advantages, and limitations across image processing tasks are comprehensively reviewed in this survey
- Large visual models (LVMs) like ViT-G, ViT-22B, Swin Transformer V2, VideoMAE V2, CLIP, and ALIGN leverage text and image encoders for learning visual and language representations through contrastive learning
- Challenges remain in the generalization ability of deep models despite advancements in LVMs and task-agnostic foundation models in computer vision research
- Future efforts should focus on enhancing the robustness and generalization capabilities of foundation models like SAM while exploring diverse applications in visual domains

## Summary

Artificial intelligence (AI) is rapidly advancing towards artificial general intelligence. This aims to mimic human-level intelligence across a wide range of tasks. To achieve this goal, the development of foundation models is crucial. One such model is the Segment Anything Model (SAM), which has made significant progress in breaking boundaries in segmentation tasks within computer vision. The Extended Anchor Concept (EAC) approach utilizes SAM in a three-phase pipeline to provide explanations for deep neural network (DNN) predictions on input images. However, there are concerns about potential negative social impacts if EAC is misapplied in sensitive domains. This could lead to misleading explanations that could misguide professionals and have severe consequences. This survey comprehensively reviews the recent progress of SAM as a foundation model for computer vision and beyond. It covers the historical development of foundation models, terminology related to SAM, and applications of SAM in various tasks and data types. The advantages and limitations of SAM across different image processing applications are analyzed, providing insights for future research to enhance foundation models like SAM. Researchers are exploring large visual models (LVMs) to enhance computer vision capabilities by scaling vision transformers and incorporating knowledge from additional modalities. This includes models like ViT-G, ViT-22B, Swin Transformer V2, VideoMAE V2, CLIP, and ALIGN that leverage text encoders and image encoders for learning visual and language representations through contrastive learning. Despite advancements in LVMs and task-agnostic foundation models in computer vision research, there are challenges related to the generalization ability of deep models. Future efforts should focus on improving the robustness and generalization capabilities of foundation models like SAM while exploring diverse applications in visual domains. Overall, this detailed summary highlights the importance of foundation models like SAM in advancing AI towards artificial general intelligence while emphasizing the need for responsible deployment to mitigate potential negative societal impacts.