Source-Free Object Detection (SFOD) aims to adapt a source-pretrained object detector to a target domain without access to source data. However, existing SFOD methods predominantly rely on internal knowledge from the source model, which limits their capacity to generalize across domains and often results in biased pseudo-labels, thereby hindering both transferability and discriminability. In contrast, Vision Foundation Models (VFMs), pretrained on massive and diverse data, exhibit strong perception capabilities and broad generalization, yet their potential remains largely untapped in the SFOD setting. In this paper, we propose a novel SFOD framework that leverages VFMs as external knowledge sources to jointly enhance feature alignment and label quality. Specifically, we design three VFM-based modules: (1) Patch-weighted Global Feature Alignment (PGFA) distills global features from VFMs using patch-similarity-based weighting to enhance global feature transferability; (2) Prototype-based Instance Feature Alignment (PIFA) performs instance-level contrastive learning guided by momentum-updated VFM prototypes; and (3) Dual-source Enhanced Pseudo-label Fusion (DEPF) fuses predictions from detection VFMs and teacher models via an entropy-aware strategy to yield more reliable supervision. Extensive experiments on six benchmarks demonstrate that our method achieves state-of-the-art SFOD performance, validating the effectiveness of integrating VFMs to simultaneously improve transferability and discriminability.
A Sensor Agnostic Domain Generalization Framework for Leveraging Geospatial Foundation Models: Enhancing Semantic Segmentation viaSynergistic Pseudo-Labeling and Generative Learning
University of Houston（休斯敦大学）；Purdue University（普渡大学）
Surgical Foundation Model Leveraging Compression and Entropy Maximization for Image-Guided Surgical Assistance
Surgical Artificial Intelligence Laboratory, Massachusetts General Hospital, MA, USA（马萨诸塞州总医院手术人工智能实验室）；Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, MA, USA（麻省理工学院计算机科学与人工智能实验室）；Department of Surgery, Duke University, NC, USA（达特茅斯大学外科系）
Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images
Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, United Kingdom（威斯敏斯特大学计算机科学系病理图像分析中心）；University Hospitals of Derby and Burton NHS Foundation Trust, United Kingdom（德比和伯顿国家健康服务基金会医院）；Department of Pathophysiology and Allergy Research, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Austria（维也纳医学大学病理学与过敏研究系）；TissueGnostics GmbH, Vienna, Austria（维也纳TissueGnostics公司）
本文提出一种结合病理学基础模型Virchow2特征与Efficient-UNet的深度学习网络，用于黑色素瘤H&E图像五类组织分割，并在PUMA Grand Challenge中夺冠，验证了基础模型加速计算病理学流程的有效性。
PeftCD: Leveraging Vision Foundation Models with Parameter-Efficient Fine-Tuning for Remote Sensing Change Detection
School of Remote Sensing and Information Engineering, Wuhan University（武汉大学遥感与信息工程学院）；School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology（南京信息工程大学遥感与地理信息工程学院）；Guangxi Water & Power Design Institute CO., Ltd.（广西水电设计院有限公司）
Beijing National Research Center for Information Science and Technology (BNRist)（北京信息科学与技术国家研究中心）；Tsinghua University（清华大学）
Current instance segmentation methods rely heavily on pixel-level annotated images. The huge cost to obtain such fully-annotated images restricts the dataset scale and limits the performance. In this paper, we formally address semi-supervised instance segmentation, where unlabeled images are employed to boost the performance. We construct a framework for semi-supervised instance segmentation by assigning pixel-level pseudo labels. Under this framework, we point out that noisy boundaries associated with pseudo labels are double-edged. We propose to exploit and resist them in a unified manner si…
It is argued that 4-valued paraconsistent truth values (called here "p-bits") can serve as a conceptual, mathematical and practical foundation for highly AI-relevant forms of probabilistic logic and probabilistic programming and concept formation. First it is shown that appropriate averaging-across-situations and renormalization of 4-valued p-bits operating in accordance with Constructible Duality (CD) logic yields PLN (Probabilistic Logic Networks) strength-and-confidence truth values. Then variations on the Curry-Howard correspondence are used to map these paraconsistent and probabilistic lo…
Evaluating Foundation Models with Pathological Concept Learning for Kidney Cancer
University of Cambridge, Cambridge, UK（剑桥大学）；Fudan University, Shanghai, China（复旦大学）；Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK（剑桥大学医院 NHS 基础信托）
MetaFruit Meets Foundation Models: Leveraging a Comprehensive Multi-Fruit Dataset for Advancing Agricultural Foundation Models
Michigan State University（密歇根州立大学）；Nanyang Technological University（南洋理工大学）；Shanghai Jiao Tong University（上海交通大学）；Pennsylvania State University（宾夕法尼亚州立大学）；United States Department of Agriculture Agricultural Research Service（美国农业部农业研究服务局）
DINO-3DRA: Leveraging 2D Foundation Model Semantics for 3D Cerebral Aneurysm Segmentation
University of Manchester（曼彻斯特大学）；Christabel Pankhurst Institute, University of Manchester（曼彻斯特大学克里斯塔贝尔·潘克赫斯特研究所）；NIHR Manchester Biomedical Research Centre（NIHR曼彻斯特生物医学研究中心）；Manchester Academic Health Sciences Centre（曼彻斯特学术健康科学中心）
Leveraging AI multimodal geospatial foundation models for improved near-real-time flood mapping at a global scale
North Carolina State University（北卡罗来纳州立大学）；LabCorp（实验室集团）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Earth and Life Institute, UCLouvain（鲁汶天主教大学地球与生命研究所）；University of Helsinki（赫尔辛基大学）；Clark University（克拉克大学）；Thünen Institute of Farm Economics（蒂嫩农场经济研究所）
Leveraging Semantic Cues from Foundation Vision Models for Enhanced Local Feature Correspondence
Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学）；Université de Bourgogne（勃艮第大学）；Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Université de Lorraine（洛林大学）；Microsoft（微软公司）
VERM: Leveraging Foundation Models to Create a Virtual Eye for Efficient 3D Robotic Manipulation
New Laboratory of Pattern Recognition (NLPR), State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别新技术实验室，多模态人工智能系统国家重点实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；FiveAges；Shandong University（山东大学）
Machine learning of microstructure--property relationships in materials leveraging microstructure representation from foundational vision transformers
Graduate Interdisciplinary Program in Applied Mathematics, University of Arizona, Tucson, AZ 85721, USA（应用数学联合交叉学科硕士项目，亚利桑那大学，图森，亚利桑那州，85721，美国）；Department of Materials Science and Engineering, University of Arizona, Tucson, AZ 85721, USA（材料科学与工程系，亚利桑那大学，图森，亚利桑那州，85721，美国）
Leveraging Foundation Model Automatic Data Augmentation Strategies and Skeletal Points for Hands Action Recognition in Industrial Assembly Lines
Faculty of Robot Science and Engineering, Northeastern University（东北大学机器人科学与工程学院）；Foshan Graduate School, Northeastern University（东北大学佛山研究生院）；College of Information Science and Engineering, Northeastern University（东北大学信息科学与工程学院）；The State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University（东北大学流程工业综合自动化国家重点实验室）
OmniAct3D: Leveraging Foundation Geometry and Evidence-Grounded Reasoning for Panoramic 3D Detection
Hunan University（湖南大学）；National Engineering Research Center of Robot Visual Perception and Control Technology（国家机器人视觉感知与控制技术工程研究中心）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
Smart Transfer: Leveraging Vision Foundation Model for Rapid Building Damage Mapping with Post-Earthquake VHR Imagery
Department of Geography, National University of Singapore（新加坡国立大学地理系）；Urban Governance and Design Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）城市治理与设计学域）；School of Environment and Spatial Informatics, China University of Mining and Technology（中国矿业大学环境与空间信息学院）；Disaster Management Institute, Istanbul Technical University（伊斯坦布尔技术大学灾害管理研究所）；Department of Complexity Science and Engineering, University of Tokyo（东京大学复杂科学与工程系）；School of Automation, Southeast University（东南大学自动化学院）
SAM-E: Leveraging Visual Foundation Model with Sequence Imitation for Embodied Manipulation
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Hong Kong University of Science and Technology（香港科技大学）；Renmin University of China（中国人民大学）；Institute of Artificial Intelligence (TeleAI), China Telecom（中国电信人工智能研究院（TeleAI））
CORONA-Fields: Leveraging Foundation Models for Classification of Solar Wind Phenomena
University of Delaware（德克萨斯大学）；Georgia State University（佐治亚州立大学）；Boston University（波士顿大学）；Catholic University of America（美国天主教大学）；Southwest Research Institute（西南研究院）；NASA Goddard Space Flight Center（NASA戈达德空间飞行中心）；Drexel University（德雷塞尔大学）
