Image restoration is a low-level visual task, and most CNN methods are designed as black boxes, lacking transparency and intrinsic aesthetics. Many unsupervised approaches ignore the degradation of visible information in low-light scenes, which will seriously affect the aggregation of complementary information and also make the fusion algorithm unable to produce satisfactory fusion results under extreme conditions. In this paper, we propose Enlighten-anything, which is able to enhance and fuse the semantic intent of SAM segmentation with low-light images to obtain fused images with good visual perception. The generalization ability of unsupervised learning is greatly improved, and experiments on LOL dataset are conducted to show that our method improves 3db in PSNR over baseline and 8 in SSIM. Zero-shot learning of SAM introduces a powerful aid for unsupervised low-light enhancement. The source code of Enlighten Anything can be obtained from https://github.com/zhangbaijin/enlighten-anything
A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering
Kyung Hee University（庆熙大学）；Beijing Institue of Technology（北京理工大学）；KAIST（韩国科学技术院）；Microsoft STCA（微软STCA）；Harbin Institute of Technology（哈尔滨工业大学）；The State University of New York - SUNY（纽约州立大学）；Hong Kong Polytechnic University（香港理工大学）
本文综述了 Meta 的 SAM 及 SAM 2 模型，分析其在图像和视频分割中的能力、局限与未来方向，重点关注高粒度分割和提示缺失场景。
SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose Estimation
DexForce Co. Ltd.（德克斯福斯有限公司）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；South China University of Technology（华南理工大学）
SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detection
Shanghai Institute of Intelligent Science and Technology, College of Electronics and Information Engineering, Tongji University（同济大学电子与信息工程学院上海智能科学技术研究院）
本文提出零样本即插即用框架 SAM-LAD，结合 SAM、对象匹配、DCGA 与异常度量模型，在多个工业和逻辑数据集上实现优于现有 SoTA 的逻辑及结构异常检测。
China Unicom (Shanghai) Industry Internet Co., Ltd.（中国联通（上海）产业互联网有限公司）；College of Intelligent Systems Science and Engineering, Harbin Engineering University（哈尔滨工程大学智能系统科学与工程学院）；Shanghai Graphic Design Information Co., Ltd（上海平面设计信息有限公司）
本文填补了Segment Anything Model (SAM)在声纳成像领域应用的研究空白，通过多种设置评估其性能，并采用带提示和语义分割的微调方法显著提升了其在自动化分割任务中的表现。
When SAM2 Meets Video Camouflaged Object Segmentation: A Comprehensive Evaluation and Adaptation
Computer Vision Laboratory, ETH Zürich（苏黎世联邦理工学院计算机视觉实验室）；Integrated System Laboratory, ETH Zürich（苏黎世联邦理工学院集成系统实验室）；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）
SAM4UDASS: When SAM Meets Unsupervised Domain Adaptive Semantic Segmentation in Intelligent Vehicles
Shanghai Jiao Tong University（上海交通大学）；Global Institute of Future Technology, Shanghai Jiao Tong University（上海交通大学全球未来技术学院）；University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University（上海交通大学密西根学院）
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；School of Biomedical Engineering, University of Science and Technology of China（中国科学技术大学生物医学工程学院）
针对 SAM 难以建模三维医学切片上下文且全体积提示成本高的问题，本文提出 Slide-SAM，利用三切片滑动窗口和相邻掩码传播提示，实现极少提示下的高效三维分割。
Medical Image Registration Meets Vision Foundation Model: Prototype Learning and Contour Awareness
The University of Sydney（悉尼大学）；Harvard Medical School（哈佛医学院）；University of Electronic Science and Technology of China（电子科技大学）；Brigham and Women’s Hospital（布莱根妇女医院）
针对无监督医学图像配准缺乏解剖知识的问题，提出融合 SAM 分割先验、原型学习与轮廓感知损失的配准框架，在多数据集上显著提升复杂结构和模糊边界场景的配准性能。
SAM Meets Robotic Surgery: An Empirical Study in Robustness Perspective
The Chinese University of Hong Kong（香港中文大学）；University College London（伦敦大学学院）；National University of Singapore（新加坡国立大学）；Hubei University of Technology（湖北工业大学）
SAM Meets Robotic Surgery: An Empirical Study on Generalization, Robustness and Adaptation
University College London（伦敦大学学院）；National University of Singapore（新加坡国立大学）；Hubei University of Technology（湖北工业大学）；The Chinese University of Hong Kong（香港中文大学）
本文在 EndoVis 2017/2018 上实证评估 SAM 的手术器械零样本分割、扰动鲁棒性与提示表现，并通过 LoRA 微调提出无需提示的 SurgicalSAM。
MatSAM: Efficient Extraction of Microstructures of Materials via Visual Large Model
University of Science and Technology Beijing（北京科技大学）；Beijing Advanced Innovation Center for Materials Genome Engineering（北京材料基因工程高精尖创新中心）；School of Intelligence Science and Technology, University of Science and Technology Beijing（北京科技大学智能科学与技术学院）
本文提出基于 SAM 的 MatSAM，通过无训练的自适应点提示与后处理实现材料显微结构零样本分割，在16个 OM/SEM 数据集上表现优于规则法并可比监督模型。
