The recent wave of foundation models has revolutionized the field of computer vision, and the segment anything model (SAM) has emerged as a particularly noteworthy advancement. SAM has not only showcased remarkable zero-shot generalization, but its applications have transcended traditional paradigms in computer vision, extending to image segmentation, multi-modal segmentation, and even the video domain.
While existing surveys have delved into SAM’s applications in image processing, there is a noticeable absence of a comprehensive review in the video domain. To bridge this gap, this work conducts a systematic review on SAM for videos in the era of foundation models. By focusing on its recent advances and discussing its applications in various tasks, this review sheds light on the opportunities for developing foundation models in the video domain.
Background: SAM and Video-related Research Domains
In order to provide readers with a clear understanding of SAM and its relevance to the video domain, this review starts with a brief introduction to the background of SAM and its applications in video-related research domains. By doing so, readers can grasp the context and significance of SAM in the broader field of computer vision.
Taxonomy of SAM Methods in Video Domain
In order to provide a structured analysis of SAM methods in the video domain, this review categorizes existing methods into three key areas: video understanding, video generation, and video editing. By organizing the methods into these categories, a clear framework is established to analyze and summarize the advantages and limitations of each approach. This taxonomy serves as a valuable resource for researchers and practitioners seeking to navigate the landscape of SAM methods in the video domain.
Comparative Analysis and Benchmarks
In order to assess the performance of SAM-based methods in comparison to the current state-of-the-art, this review provides a comprehensive analysis of comparative results on representative benchmarks. By evaluating the performance of SAM-based methods against existing approaches, readers gain insights into the strengths and weaknesses of SAM in the video domain. This comparative analysis contributes to the development of benchmarks and establishes a baseline for future research and advancements.
Challenges and Future Research Directions
While SAM has shown immense promise and achieved impressive results in the video domain, there are still challenges that need to be addressed. This review discusses the challenges faced by current research in SAM for videos and outlines several future research directions. By pinpointing the existing gaps and envisioning future possibilities, this review acts as a catalyst for further advancements and innovation in the field.
In conclusion, this systematic review of SAM for videos in the era of foundation models addresses a notable gap in the existing literature. By providing a comprehensive analysis of SAM’s applications, comparative results, and future research directions, this review serves as a valuable resource for researchers, practitioners, and enthusiasts interested in the intersection of computer vision, foundation models, and the video domain.