Adapting Depth Anything to Adverse Imaging Conditions with Events
National Key Lab of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（multispectral information intelligent processing technology 国家重点实验室，人工智能与自动化学院，华中科技大学）；School of Computing, National University of Singapore（computing 学院，新加坡国立大学）；School of Computer Science and Engineering, Sun Yat-Sen University（computer science and engineering 学院，中山大学）
Shihan Peng, Yuyang Xiong, Hanyu Zhou, Zhiwei Shi, Haoyue Liu, Gang Chen, Luxin Yan, Yi Chang
备注：
This work has been submitted to the IEEE for possible publication
Robust depth estimation under dynamic and adverse lighting conditions is essential for robotic systems. Currently, depth foundation models, such as Depth Anything, achieve great success in ideal scenes but remain challenging under adverse imaging conditions such as extreme illumination and motion blur. These degradations corrupt the visual signals of frame cameras, weakening the discriminative features of frame-based depths across the spatial and temporal dimensions. Typically, existing approaches incorporate event cameras to leverage their high dynamic range and temporal resolution, aiming to compensate for corrupted frame features. However, such specialized fusion models are predominantly trained from scratch on domain-specific datasets, thereby failing to inherit the open-world knowledge and robust generalization inherent to foundation models. In this work, we propose ADAE, an event-guided spatiotemporal fusion framework for Depth Anything in degraded scenes. Our design is guided by two key insights: 1) Entropy-Aware Spatial Fusion. We adaptively merge frame-based and event-based features using an information entropy strategy to indicate illumination-induced degradation. 2) Motion-Guided Temporal Correction. We resort to the event-based motion cue to recalibrate ambiguous features in blurred regions. Under our unified framework, the two components are complementary to each other and jointly enhance Depth Anything under adverse imaging conditions. Extensive experiments have been performed to verify the superiority of the proposed method. Our code will be released upon acceptance.
Adapting a Foundation Model for Lunar Surface Height Estimation
University of Technology of Troyes（特鲁瓦技术大学）；Hochschule Darmstadt（达姆施塔特应用科学大学）；European Space Agency（欧洲空间局）；Technische Hochschule Würzburg-Schweinfurt（维尔茨堡-施韦因富特应用科学大学）
SAMamba3D: adapting Segment Anything for generalizable 3D segmentation of multiphase pore-scale images
Department of Earth Science and Engineering, Imperial College London（帝国理工学院伦敦地球科学与工程系）；College of Artificial Intelligence, China University of Petroleum (Beijing)（中国石油大学（北京）人工智能学院）；College of Petroleum Engineering, China University of Petroleum (Beijing)（中国石油大学（北京）石油工程学院）
Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images
Information Engineering University（信息工程大学）；Nanjing University of Information Science and Technology（南京信息工程大学）；ETH Zurich（苏黎世联邦理工学院）；University of Trento（特伦托大学）
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。
SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More
KOKONI, Moxin (Huzhou) Tech. Co., LTD（科科尼（湖州）魔芯科技有限公司）；School of Information Engineering, Huzhou University（湖州大学信息工程学院）；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技术学院）；School of Software Technology, Zhejiang University（浙江大学软件学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Information Systems Technology and Design Pillar, Singapore University of Technology and Design（新加坡科技设计大学信息系统技术与设计支柱学院）
SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More
KOKONI, Moxin (Huzhou) Tech. Co., LTD（莫西（湖州）科技有限公司KOKONI）；Beihang University（北京航空航天大学）；Zhejiang University（浙江大学）；Huzhou University（湖州师范学院）；Singapore University of Technology and Design（新加坡科技设计大学）
Adapting Segment Anything Model for Power Transmission Corridor Hazard Segmentation
School of Computer Science, National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence（计算机学院、多媒体软件国家工程研究中心、人工智能研究所）；Hubei Key Laboratory of Multimedia（湖北多媒体重点实验室）；Network Communication Engineering, Wuhan University, Wuhan, China（网络通信工程、武汉大学、中国武汉）；Institute of Power Transmission（输变电研究所）；Institute of Power Transmission and Transformation Technology, State Grid Zhejiang Electric Power Co., LTD, Research Institute Hangzhou, China（输变电技术研究所、国家电网浙江电力有限公司、杭州研究院、中国）
RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentation
College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机学院）；School of Computer Science, The University of Sydney（悉尼大学计算机学院）
Adapting Segment Anything Model 3 for Concept-Driven Lesion Segmentation in Medical Images: An Experimental Study
The Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心放射肿瘤学系医学人工智能与自动化（MAIA）实验室）；Medical Image Processing Group, Department of Radiology, University of Pennsylvania（宾夕法尼亚大学放射学系医学图像处理组）；Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心内科学系消化与肝脏疾病科）
EndoSfM3D: Learning to 3D Reconstruct Any Endoscopic Surgery Scene using Self-supervised Foundation Model
UCL Hawkes Institute（UCL哈维斯研究所）；Department of Medical Physics（医学物理系）；Biomedical Engineering, University College London, UK（生物医学工程，伦敦大学学院，英国）；Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester, UK（信息学、成像与数据科学部门，曼彻斯特大学，曼彻斯特，英国）
Adapting a Segmentation Foundation Model for Medical Image Classification
Department of Computer Science, University of Texas Rio Grande Valley（德克萨斯理工大学里奥格兰德分校计算机科学系）；Department of Computer Science, Texas Woman’s University（德克萨斯女性大学计算机科学系）；Sewickley Academy（塞维克利学院）；Department of Mathematical Sciences, The University of Texas at Dallas（德克萨斯大学达拉斯分校数学科学系）；Department of Computer Science and Engineering, University of Notre Dame（诺特丹大学计算机科学与工程系）
本文提出将 SAM 图像编码器作为冻结特征提取器，并设计空间局部化通道注意力机制 SLCA，将其集成到深度学习分类模型中，以增强对关键区域的关注，在三个公开医学图像分类数据集上验证了方法的有效性和数据高效性。
Adapting SAM with Dynamic Similarity Graphs for Few-Shot Parameter-Efficient Small Dense Object Detection: A Case Study of Chickpea Pods in Field Conditions
McGill University（麦吉尔大学）；Michigan State University（密歇根州立大学）
Adverse weather conditions such as haze and rain corrupt the quality of captured images, which cause detection networks trained on clean images to perform poorly on these images. To address this issue, we propose an unsupervised prior-based domain adversarial object detection framework for adapting the detectors to hazy and rainy conditions. In particular, we use weather-specific prior knowledge obtained using the principles of image formation to define a novel prior-adversarial loss. The prior-adversarial loss used to train the adaptation process aims to reduce the weather-specific informatio…
