Align3D-AD: Cross-Modal Feature Alignment and Dual-Prompt Learning for Zero-shot 3D Anomaly Detection
Smart Manufacturing Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）智能制造方向）；The Hong Kong University of Science and Technology（香港科技大学）；College of Mechanical and Vehicle Engineering, Hunan University（湖南大学机械与车辆工程学院）
Zero-shot 3D anomaly detection aims to identify anomalies without access to training data from target categories. However, existing methods mainly rely on projecting 3D observations into multi-view representations that primarily capture geometric cues rather than realistic visual semantics and process them with vision encoders pretrained on RGB data, leading to a significant domain gap between the encoder and the projected representations. To address this issue, we propose Align3D-AD, a unified two-stage framework that leverages the RGB modality from auxiliary categories as cross-modal guidance for zero-shot 3D anomaly detection. First, we introduce a cross-modal feature alignment paradigm that maps rendering features into the RGB semantic space. Unlike prior works that implicitly rely on pretrained encoders, our method enables direct semantic transfer from RGB observations. A semantic consistency reweighting strategy is further introduced to refine feature alignment by reweighting local regions according to holistic semantic consistency. Second, we propose a modality-aware prompt learning framework with dual-prompt contrastive alignment. By assigning independent prompts to RGB-aligned and rendering features, our method captures complementary semantics across modalities, while the contrastive alignment further enhances prompt representations to improve discriminability. Extensive experiments on MVTec3D-AD, Eyecandies, and Real3D-AD demonstrate that Align3D-AD consistently outperforms existing zero-shot methods under both one-vs-rest and cross-dataset settings, highlighting its generalization capability and robustness. Code and the dataset will be made available once our paper is accepted.
DAS3D: Dual-modality Anomaly Synthesis for 3D Anomaly Detection
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Microsoft Research Asia（微软亚洲研究院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）
AF-CLIP: Zero-Shot Anomaly Detection via Anomaly-Focused CLIP Adaptation
School of Computer Science and Engineering（计算机科学与工程学院）；Sun Yat-sen University（中山大学）；Guangdong Key Laboratory of Big Data Analysis and Processing（大数据分析与处理广东省重点实验室）
COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection
Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局信息通信研究院）；Centre for Frontier AI Research (CFAR), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局前沿人工智能研究中心）
PSMP-CLIP: Patch-Prompt SAM and Multi-Semantic Prompting for CLIP-Based Zero-Shot Anomaly Detection
Harbin Engineering University（哈尔滨工程大学）；Key Laboratory of Advanced Marine Communication and Information Technology（先进海洋通信与信息技术重点实验室）；University of Toronto（多伦多大学）；Kanagawa University（神奈川大学）
CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generation
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence and Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（西安交通大学人机混合增强智能全国重点实验室与人工智能与机器人研究所）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））；Xiamen University（厦门大学）；Shenzhen University（深圳大学）
AnyAD: Unified Any-Modality Anomaly Detection in Incomplete Multi-Sequence MRI
School of Computer Science and Technology, Hangzhou Dianzi University（计算机科学与技术学院，杭州电子科技大学）；School of Biomedical Engineering, Tsinghua University（生物医学工程学院，清华大学）；HDU-ITMO Joint Institute, Hangzhou Dianzi University（杭州电子科技大学-ITMO联合研究所）；School of Artificial Intelligence and Robotics, Hunan University（人工智能与机器人学院，湖南大学）
Cross-Modal Learning for Anomaly Detection in Complex Industrial Process: Methodology and Benchmark
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, P. R. China（合成过程工业系统自动化国家重点实验室，东北大学，沈阳110819，中华人民共和国）；School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia（软件与电子工程学院，斯威本科技大学，墨尔本，VIC 3122，澳大利亚）
AnoRefiner: Anomaly-Aware Group-Wise Refinement for Zero-Shot Industrial Anomaly Detection
School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；Hubei Key Laboratory of Smart Internet Technology, Huazhong University of Science and Technology（华中科技大学智能互联网技术重点实验室）；Artificial Intelligence Research Institute, Wuhan JingCe Electronic Group Co., LTD（武汉景创电子集团有限公司人工智能研究所）；Department of Information Engineering and Computer Science, University of Trento（特伦托大学信息工程与计算机科学系）
Shanghai Jiao Tong University（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；National University of Singapore（新加坡国立大学）；King’s College London（伦敦国王学院）
VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detection
University of Science and Technology of China（中国科学技术大学）；Northeastern University（东北大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
Enhancing Zero-Shot Anomaly Detection: CLIP-SAM Collaboration with Cascaded Prompts
School of Artificial Intelligence, Anhui University, Hefei, China（人工智能学院，安徽大学，合肥，中国）；Anhui Provincial Key Laboratory of Security Artificial Intelligence, Anhui University（安徽省安全人工智能重点实验室，安徽大学）；Anhui Provincial Engineering Research Center for Unmanned System（安徽省无人系统与智能技术工程研究中心）
本文提出一种用于工业异常检测的两阶段零样本异常分割框架，协同利用 CLIP 的异常定位与 SAM 的边界感知能力，通过 PPG 和 CPS 模块优化点提示与分割精度，在 Visa 等数据集上取得 SOTA 表现。
MadCLIP: Few-shot Medical Anomaly Detection with CLIP
Department of Computer Science, University of Verona（威尼斯大学计算机科学系）；AI for Good (AIGO) Research Unit, Istituto Italiano di Tecnologia（意大利技术研究院人工智能为善研究单元）
FiLo++: Zero-/Few-Shot Anomaly Detection by Fused Fine-Grained Descriptions and Deformable Localization
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（基础模型研究中心，自动化研究所，中国科学院）；School of Artifcial Intelligence, University of Chinese Academy of Sciences（人工智能学院，中国科学院大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Wuhan AI Research（武汉AI研究所）；Peng Cheng Laboratory（鹏城实验室）；Guangdong Provincial Key Laboratory of Intellectual Property & Big Data, Guangdong Polytechnic Normal University（广东省知识产权与大数据重点实验室，广东工业大学）
UltraAD: Fine-Grained Ultrasound Anomaly Classification via Few-Shot CLIP Adaptation
Computer Aided Medical Procedures (CAMP)（计算机辅助医疗程序）；TU Munich, Germany（慕尼黑工业大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；The First Affiliated Hospital of Sun Yat-Sen University（中山大学附属第一医院）
AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIP
School of Biomedical Engineering, Division of Life Sciences and Medicine, USTC（中国科学技术大学生命科学与医学部生物医学工程学院）；MIRACLE Center, Suzhou Institute for Advance Research, USTC（中国科学技术大学苏州高等研究院MIRACLE中心）；Key Laboratory of Intelligent Information Processing of CAS, ICT, CAS（中国科学院计算技术研究所中国科学院智能信息处理重点实验室）；State Key Laboratory of Precision and Intelligent Chemistry, USTC（中国科学技术大学精准与智能化学国家重点实验室）；Stanford University（斯坦福大学）
Dual-Modality Vehicle Anomaly Detection via Bilateral Trajectory Tracing
Wuhan University（武汉大学）
交通异常检测在智能交通系统（ITS）中起着至关重要的作用。这项任务的主要挑战在于高度多样化的异常场景和多变的照明条件。虽然许多工作已经成功地识别了同质天气和场景中的异常，但很少有人解决复杂的问题。提出了一种基于双模态模块化的异常车辆鲁棒检测方法。我们介绍了一个集成的异常检测框架，包括以下模块：背景建模，车辆跟踪与检测，掩模构造，感兴趣区域（ROI）回溯，和双模态跟踪。具体地说，我们使用背景建模来过滤运动信息，并留下静态信息用于后续的车辆检测。在车辆检测和跟踪模块中，我们采用YOLOv 5和多尺度跟踪来定位异常。此外，我们利用帧间差分和跟踪结果来识别道路，并获得掩模。此外，我们引入了多个相似性估计指标，通过回溯来细化异常周期。最后，我们提出了一个双模态双边跟踪模块，以进一步细化的时间。在NVIDIA 2021 AI City Challenge的Track 4测试集上进行的实验产生了0.9302 F1-Score和3.4039均方根误差（RMSE）的结果，表明了我们框架的有效性。
