本文评估 SAM 在虚拟现实眼部图像上的零样本分割能力，验证边界框和点提示可提升性能，并表明其在部分眼部特征上可媲美专用模型。
摘要：
基础模型的出现标志着人工智能进入了一个新时代。Segment Anything Model（SAM）是首个用于图像分割的基础模型。在本研究中，我们评估了 SAM 从虚拟现实装置记录的眼部图像中分割特征的能力。对带标注眼部图像数据集日益增长的需求，为 SAM 重新定义视线估计领域的数据标注格局提供了重要机遇。我们的研究集中于 SAM 的零样本学习能力，以及边界框或点点击等提示的有效性。我们的结果与其他领域的研究一致，表明根据所分割特征的不同，SAM 的分割效果可以与专用模型相媲美，且提示能够提升其性能；在一个数据集中，瞳孔分割的 IoU 达到 93.34%。像 SAM 这样的基础模型可能通过实现快速、简便的图像分割，减少对专用模型和大量人工标注的依赖，从而革新视线估计。
英文摘要：
The advent of foundation models signals a new era in artificial intelligence. The Segment Anything Model (SAM) is the first foundation model for image segmentation. In this study, we evaluate SAM's ability to segment features from eye images recorded in virtual reality setups. The increasing requirement for annotated eye-image datasets presents a significant opportunity for SAM to redefine the landscape of data annotation in gaze estimation. Our investigation centers on SAM's zero-shot learning abilities and the effectiveness of prompts like bounding boxes or point clicks. Our results are consistent with studies in other domains, demonstrating that SAM's segmentation effectiveness can be on-par with specialized models depending on the feature, with prompts improving its performance, evidenced by an IoU of 93.34% for pupil segmentation in one dataset. Foundation models like SAM could revolutionize gaze estimation by enabling quick and easy image segmentation, reducing reliance on specialized models and extensive manual annotation.
Eye image segmentation using visual and concept prompts with Segment Anything Model 3 (SAM3)
Lund University Humanities Lab \& Dept. of Psychology, Lund University Lund Sweden；Lund University Humanities Lab Lund Sweden；Lund University Humanities Lab \& Dept. of Psychology, Lund University；Lund University Humanities Lab
SAMAug: Point Prompt Augmentation for Segment Anything Model
School of Computing, University of Georgia（佐治亚大学计算机学院）；School of Automation, Northwestern Polytechnical University（西北工业大学自动化学院）；Department of Computer Science and Engineering, Lehigh University（里海大学计算机科学与工程系）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Department of Computer Science and Engineering, University of Texas at Arlington（德克萨斯大学阿灵顿分校计算机科学与工程系）；Department of Radiation Oncology, Mayo Clinic（梅奥诊所放射肿瘤科）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）
SAM3-UNet: Simplified Adaptation of Segment Anything Model 3
Sun Yat-sen University（中山大学）；Jiangxi Normal University（江西师范大学）；Hainan University（海南大学）；Dalian University of Technology（大连理工大学）；Chizhou University（池州学院）；Wuhan University（武汉大学）
Nanyang Technological University（南洋理工大学）；SenseTime Research（商汤科技研究院）；Carnegie Mellon University（卡内基梅隆大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with over 1 billion masks on 11M licensed and privacy respecting images. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks. We evaluate its capabilities on numerous tasks and find that its zero-shot performance is impressive -- often competitive with or even superior to prior fully supervised results. We are releas…
AI-SAM: Automatic and Interactive Segment Anything Model
The Pennsylvania State University（宾夕法尼亚州立大学）；Huck Institutes of the Life Sciences（哈克生命科学研究所）；Northwestern Memorial Hospital（西北纪念医院）；Northwestern University（西北大学）
Biomedical SAM 2: Segment Anything in Biomedical Images and Videos
University of Georgia（佐治亚大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Lehigh University（理海大学）；Northeastern University（东北大学）；University of Notre Dame（圣母大学）
Duke University（杜克大学）；Duke Kunshan University（昆山杜克大学）；Nicholas Institute for Energy, Environment & Sustainability, Duke University（杜克大学尼古拉斯能源、环境与可持续发展研究所）；University of Montana（蒙大拿大学）
On Efficient Variants of Segment Anything Model: A Survey
School of Computer Science and Engineering（计算机科学与工程学院）；School of Computing and Communications（计算与通信学院）；School of Computer Science and Technology（计算机科学与技术学院）
SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model
Wuhan University（武汉大学）；The University of Sydney（悉尼大学）；National Engineering Research Center of Speech and Language Information Processing（国家语音及语言信息处理工程技术研究中心）
Segment Anything Model Can Not Segment Anything: Assessing AI Foundation Model's Generalizability in Permafrost Mapping
Arizona State University（亚利桑那州立大学）；Woodwell Climate Research Center（伍德韦尔气候研究中心）；University of Connecticut（康涅狄格大学）；U.S. Geological Survey（美国地质调查局）；University of California, Santa Barbara（加利福尼亚大学圣巴巴拉分校）；University of Illinois at Urbana-Champaign（伊利诺伊大学香槟分校）
本文评估了以 Meta SAM 为代表的视觉基础模型在永久冻土地貌分割中的表现，通过多种提示策略和微调测试其零样本能力与领域适应性，并指出其在挑战性地理空间任务中仍需改进。
Generalization vs. Specialization: Evaluating Segment Anything Model (SAM3) Zero-Shot Segmentation Against Fine-Tuned YOLO Detectors
Cornell University, Department of Biological and Environmental Engineering（康奈尔大学生物与环境工程系）；University of the Peloponnese, Department of Informatics and Telecommunications（希腊皮埃里亚大学信息与电信系）
SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；School of Computer and Information Engineering, Jiangxi Normal University（江西师范大学计算机与信息工程学院）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Tianjin University（天津大学）；Monash University（墨尔本大学）；Pazhou Lab（琶洲实验室）；Smart Hospital Research Institute, Peking University Shenzhen Hospital（北京大学深圳医院智能医院研究所）
