Summary of Segment Anything For Videos: a Systematic Survey, by Chunhui Zhang et al.
Segment Anything for Videos: A Systematic Survey
by Chunhui Zhang, Yawen Cui, Weilin Lin, Guanjie Huang, Yan Rong, Li Liu, Shiguang Shan
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The recent surge in foundation models has led to impressive results in computer vision (CV) and beyond, with the Segment Anything Model (SAM) gaining significant attention. SAM’s remarkable zero-shot generalization capabilities have challenged traditional paradigms in CV, achieving exceptional performance in various image segmentation tasks, as well as text-to-mask and multi-modal segmentation. The latest released SAM 2 has sparked enthusiasm for promptable visual segmentation in both images and videos. However, existing surveys primarily focus on SAM’s applications in image processing tasks, leaving a gap in the video domain. This work addresses this gap by conducting a systematic review of SAM for videos in the era of foundation models. The paper begins with an introduction to SAM and its background in video-related research domains. A systematic taxonomy categorizes existing methods into three key areas: video understanding, video generation, and video editing, analyzing their advantages and limitations. Comparative results of SAM-based and current state-of-the-art methods on representative benchmarks are provided, along with insightful analysis. The paper concludes by discussing the challenges faced by current research and envisioning future research directions in the field of SAM for video and beyond. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SAM is a type of foundation model that has been successful in computer vision and other areas. It can do many tasks without being trained on specific data, which is helpful. The latest version of SAM, called SAM 2, is good at doing visual segmentation tasks with prompts. However, most research on SAM has focused on image processing tasks, not video tasks. This paper looks at how SAM works for videos and what it can do. It also compares SAM to other state-of-the-art methods. |
Keywords
» Artificial intelligence » Attention » Generalization » Image segmentation » Mask » Multi modal » Sam » Zero shot
