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）
Jiaxing Huang, Kai Jiang, Jingyi Zhang, Han Qiu, Lewei Lu, Shijian Lu, Eric Xing
像 SEEM 和 SAM 这样的 Segment Anything Models 在学习分割一切方面展现了巨大潜力。SAM 的核心设计在于 Promptable Segmentation，它接受手工制作的提示作为输入，并返回预期的分割掩码。SAM 使用两类提示，包括空间提示（例如点）和语义提示（例如文本），它们共同作用以提示 SAM 在下游数据集上分割一切。尽管提示很重要，但如何为 SAM 获取合适的提示在很大程度上仍未被充分探索。在这项工作中，我们检查了 SAM 的架构，并识别出为 SAM 学习有效提示的两个挑战。为此，我们提出了空间-语义提示学习（SSPrompt），它学习有效的语义和空间提示以构建更好的 SAM。具体而言，SSPrompt 引入了空间提示学习和语义提示学习，直接在嵌入空间上优化空间提示和语义提示，并选择性地利用预训练提示编码器中编码的知识。大量实验表明，SSPrompt 在多个广泛采用的数据集上持续实现了卓越的图像分割性能。
英文摘要：
Segment Anything Models (SAMs) like SEEM and SAM have demonstrated great potential in learning to segment anything. The core design of SAMs lies with Promptable Segmentation, which takes a handcrafted prompt as input and returns the expected segmentation mask. SAMs work with two types of prompts including spatial prompts (e.g., points) and semantic prompts (e.g., texts), which work together to prompt SAMs to segment anything on downstream datasets. Despite the important role of prompts, how to acquire suitable prompts for SAMs is largely under-explored. In this work, we examine the architecture of SAMs and identify two challenges for learning effective prompts for SAMs. To this end, we propose spatial-semantic prompt learning (SSPrompt) that learns effective semantic and spatial prompts for better SAMs. Specifically, SSPrompt introduces spatial prompt learning and semantic prompt learning, which optimize spatial prompts and semantic prompts directly over the embedding space and selectively leverage the knowledge encoded in pre-trained prompt encoders. Extensive experiments show that SSPrompt achieves superior image segmentation performance consistently across multiple widely adopted datasets.
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。
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）
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）
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）
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）
HyPSAM: Hybrid Prompt-driven Segment Anything Model for RGB-Thermal Salient Object Detection
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）；School of Information Science and Engineering, Yunnan University（云南大学信息科学与工程学院）；School of Mathematics, Southeast University（东南大学数学学院）
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）
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）
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…
