本文通过向 SAM 2 输入多种复杂视频变换并分析各阶段响应，揭示其逐层过滤噪声、突出目标对象的机制，并提供数据集、阶段分析与可视化。
摘要：
视频对象分割（VOS）是视频感知与理解发展中的一项关键任务。由 Meta AI 发布的 Segment-Anything Model 2（SAM 2）是当前用于端到端 VOS 的最先进架构。SAM 2 在干净视频数据和增强数据上都表现得非常好，而完全智能的视频感知需要理解该架构如何能够取得如此高质量的结果。为了更好地理解 SAM 2 架构中的每个步骤如何实现高质量视频分割，研究将多种复杂视频变换输入该架构，并测量该过程中每个阶段所受到的影响。研究观察到，每个递进阶段都能够过滤复杂变换噪声并突出感兴趣对象。贡献包括创建复杂变换视频数据集、分析 SAM 2 架构的每个阶段如何解释这些变换，以及可视化经过每个阶段分割出的对象。通过更好地理解每种模型结构如何影响整体视频理解，VOS 的开发可以致力于提升现实世界适用性，以及在复杂杂乱场景和遮挡情况下对对象进行跟踪、定位和分割的性能。
英文摘要：
Video object segmentation (VOS) is a critical task in the development of video perception and understanding. The Segment-Anything Model 2 (SAM 2), released by Meta AI, is the current state-of-the-art architecture for end-to-end VOS. SAM 2 performs very well on both clean video data and augmented data, and completely intelligent video perception requires an understanding of how this architecture is capable of achieving such quality results. To better understand how each step within the SAM 2 architecture permits high-quality video segmentation, a variety of complex video transformations are passed through the architecture, and the impact at each stage of the process is measured. It is observed that each progressive stage enables the filtering of complex transformation noise and the emphasis of the object of interest. Contributions include the creation of complex transformation video datasets, an analysis of how each stage of the SAM 2 architecture interprets these transformations, and visualizations of segmented objects through each stage. By better understanding how each model structure impacts overall video understanding, VOS development can work to improve real-world applicability and performance tracking, localizing, and segmenting objects despite complex cluttered scenes and obscurations.
ViPSAM: Visual Prompting Medical Image Segmentation Using Segment Anything Model
Department of Artificial Intelligence, Sungkyunkwan University, Republic of Korea（人工智能系，成均馆大学，大韩民国）；Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Republic of Korea（放射肿瘤科，三星医疗中心，成均馆大学医学院，大韩民国）；Department of MetaBioHealth, Sungkyunkwan University, Republic of Korea（元生物健康系，成均馆大学，大韩民国）
Segment Anything in Medical Images and Videos: Benchmark and Deployment
Peter Munk Cardiac Centre, University Health Network（彼得·芒克心脏中心，大学健康网络）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；AI Hub, University Health Network（人工智能中心，大学健康网络）；University of Toronto（多伦多大学）
本文对 SAM2 在 11 种医学图像模态和视频上进行了全面基准测试，并通过微调迁移学习及 3D Slicer 插件和 Gradio API 部署，验证了其快速适应医学领域并实现高效 3D 分割的能力。
SAM3D: Segment Anything Model in Volumetric Medical Images
University of Arkansas（阿肯色大学）；University of Science, Vietnam National University（越南国家大学科学大学）；John von Neumann Institute, Vietnam National University（越南国家大学约翰·冯·诺依曼研究所）；West Virginia University（西弗吉尼亚大学）；University of Arkansas for Medical Sciences（阿肯色医科大学）
Mask-Enhanced Segment Anything Model for Tumor Lesion Semantic Segmentation
Beihang University（北京航空航天大学）；Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；The Chinese University of Hong Kong（香港中文大学）；Sun Yat-sen University（中山大学）；Peking University Cancer Hospital & Institute（北京大学肿瘤医院暨研究所）；Nanjing University（南京大学）
Zhejiang University（浙江大学）；Westlake University（西湖大学）；University of Electronic Science and Technology of China（电子科技大学）；Pennsylvania State University（宾夕法尼亚州立大学）
Segment Anything Model (SAM) for Radiation Oncology
Mayo Clinic（梅奥诊所）；University of Georgia（佐治亚大学）；The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
SQA-SAM: Segmentation Quality Assessment for Medical Images Utilizing the Segment Anything Model
Nanjing University of Science and Technology（南京理工大学）；Digital Medical Research Center, School of Basic Medical Sciences, Fudan University（复旦大学基础医学院数字医学研究中心）；The Chinese University of Hong Kong（香港中文大学）；University of Notre Dame（圣母大学）
Improving Segment Anything on the Fly: Auxiliary Online Learning and Adaptive Fusion for Medical Image Segmentation
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；Digital Medical Research Center, School of Basic Medical Sciences, Fudan University（复旦大学基础医学院数字医学研究中心）；Shanghai Jiaotong University（上海交通大学）；the Chinese University of Hong Kong（香港中文大学）
