University of Alberta（阿尔伯塔大学）；Tencent WeChat（腾讯微信）；Northwestern University（西北大学）；SUSTech（四川大学）；Yale University（耶鲁大学）；Utrecht University（乌得勒支大学）；Dalian University of Technology（大连理工大学）；SIAT, Chinese Academy of Sciences（中科院深圳先进技术研究院）
Segment Anything Model 3 (SAM3) advances open-vocabulary segmentation through promptable concept segmentation, enabling users to segment all instances associated with a given concept using short noun-phrase (NP) prompts. While effective for concept-level grounding, real-world interactions often involve far richer natural-language instructions that combine attributes, relations, actions, states, or implicit reasoning. Currently, SAM3 relies on external multi-modal agents to convert complex instructions into NPs and conducts iterative mask filtering, leading to coarse representations and limited instance specificity. In this work, we present SAM3-I, an instruction-following extension of the SAM family that unifies concept-level grounding and instruction-level reasoning within a single segmentation framework. Built upon SAM3, SAM3-I introduces an instruction-aware cascaded adaptation mechanism with dedicated alignment losses that progressively aligns expressive instruction semantics with SAM3's vision-language representations, enabling direct interpretation of natural-language instructions while preserving its strong concept recall ability. To enable instruction-following learning, we introduce HMPL-Instruct, a large-scale instruction-centric dataset that systematically covers hierarchical instruction semantics and diverse target granularities. Experiments demonstrate that SAM3-I achieves appealing performance across referring and reasoning-based segmentation, showing that SAM3 can be effectively extended to follow complex natural-language instructions without sacrificing its original concept-driven strengths. Code and dataset are available at https://github.com/debby-0527/SAM3-I.
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）
TASAM: Terrain-and-Aware Segment Anything Model for Temporal-Scale Remote Sensing Segmentation
University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；Wuhan University（武汉大学）；Dalian University of Technology（大连理工大学）；University of Chinese Academy of Sciences（中国科学院大学）
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）
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。
Cross Prompting Consistency with Segment Anything Model for Semi-supervised Medical Image Segmentation
The Chinese University of Hong Kong（香港中文大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Institute of Medical Intelligence and XR, The Chinese University of Hong Kong（香港中文大学医学智能与XR研究所）
提出 CPC-SAM，在双分支框架中通过 SAM 交叉提示和提示一致性正则化利用标注与未标注数据，显著提升半监督医学图像分割性能。
MergeSAM: Unsupervised change detection of remote sensing images based on the Segment Anything Model
School of Geography and Planning, Sun Yat-sen University（中山大学地理科学与规划学院）；Unit 33269 of the PLA（中国人民解放军33269部队）；Intelligent Science & Technology Academy Limited of CASIC（中国航天科工集团智能科技研究院有限公司）
Enhancing the Reliability of Segment Anything Model for Auto-Prompting Medical Image Segmentation with Uncertainty Rectification
Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与孵化研究院）；Shanghai Academy of Artificial Intelligence for Science（上海科学人工智能研究院）
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）
Slender Object Scene Segmentation in Remote Sensing Image Based on Learnable Morphological Skeleton with Segment Anything Model
School of Mathematical Sciences, Beijing Normal University（北京师范大学数学科学学院）；State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University（北京师范大学地理科学学部遥感科学国家重点实验室）
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）
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）
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）
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）
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）
Multi-Sequence Parotid Gland Lesion Segmentation via Expert Text-Guided Segment Anything Model
Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University（中山大学附属第一医院放射科）；Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center（中山大学南方学院肿瘤学国家重点实验室、癌症医学创新中心、中山大学肿瘤中心放射科）；Department of Radiology, Shantou Central Hospital（汕头市中心医院放射科）；Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University（广东省恶性肿瘤表观遗传学与基因调控重点实验室、中山大学附属第一医院医学研究中心、中山大学）；Department of Radiology, Shenshan Medical Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University（中山大学附属山田医院放射科）
I-MedSAM: Implicit Medical Image Segmentation with Segment Anything
Peking University（北京大学）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Beihang University（北京航空航天大学）；Beijing University of Posts and Telecommunications（北京邮电大学）
Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation
University of Oxford（牛津大学）；National University of Singapore（新加坡国立大学）；University of Alberta（阿尔伯塔大学）；A*STAR（新加坡科技研究局）；Carnegie Mellon University（卡内基梅隆大学）；Singapore Eye Research Institute（新加坡眼科研究所）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of Hongkong（香港大学）
本文提出Medical SAM Adapter（Med-SA），通过轻量适配技术将医学知识融入SAM，利用SD-Trans和HyP-Adpt适配2D模型至3D医学图像，在17个任务上以仅2%参数更新超越SOTA方法。
Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation
National Engineering Research Center for Multimedia Software（国家多媒体软件工程技术研究中心）；Medical Artificial Intelligence Research Institute of Renmin Hospital, Wuhan University（武汉大学人民医院医学人工智能研究院）；School of Computer Science, Central China Normal University（华中师范大学计算机学院）
SAM3-Adapter: Efficient Adaptation of Segment Anything 3 for Camouflage Object Segmentation, Shadow Detection, and Medical Image Segmentation
KOKONI, Moxin (Huzhou) Tech. Co., LTD（摩西（湖州）科技有限公司）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；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）；School of Information Engineering, Huzhou University（湖州大学信息工程学院）；School of Electrical and Electronic Engineering, Nanyang Technological University（新加坡南洋理工大学电子与电气工程学院）；College of Computing and Data Science, Nanyang Technological University（新加坡南洋理工大学计算与数据科学学院）；School of Information Science and Technology, University of Science and Technology of China（中国科学技术大学信息科学与技术学院）
