Yunkang Cao (曹云康)

About个人简介

I am an Assistant Professor and Ph.D. supervisor at the School of Artificial Intelligence and Robotics, Hunan University (HNU), an Associate Research Fellow at the National Engineering Research Center of Robot Visual Perception and Control Technology, and Deputy Director of the Department of Robotics Engineering. I am a core member of the research team led by Yaonan Wang (王耀南院士) and Hui Zhang (张辉院长).

曹云康，湖南大学人工智能与机器人学院助理教授、博士生导师，机器人视觉感知与控制技术国家工程研究中心副研究员，机器人工程系副主任，王耀南院士、张辉院长团队核心成员。

I received my Ph.D. in Mechanical Engineering from Huazhong University of Science and Technology, where I was advised by Prof. Weiming Shen. From 2023 to 2024, I was a visiting Ph.D. researcher at Politecnico di Milano under the supervision of Prof. Giacomo Boracchi.

2025 年获华中科技大学机械工程博士学位，师从沈卫明教授。博士期间于 2023 至 2024 年赴米兰理工大学访学，合作导师为 Giacomo Boracchi 教授。

My research centers on industrial inspection and covers four connected directions: anomaly generation, anomaly detection, anomaly understanding, and embodied perception. I study how inspection systems can learn from limited defect data, detect and explain anomalies in open environments, and guide robots to gather evidence through active observation. The goal is to develop practical methods that connect perception, reasoning, and action in industrial settings.

主要研究工业场景中的异常生成、异常检测、异常理解和具身感知。针对缺陷数据少、生产环境变化大等问题，研究可控缺陷生成、开放场景异常检测和多模态异常推理，并将相关方法用于机器人主动巡检。代表性工作包括 Anomagic、INP-Former、IAD-R1 等。

Education and Experience学习与工作经历

• 2025.05 - Present, Assistant Professor / Associate Research Fellow, School of Artificial Intelligence and Robotics, Hunan University.2025.05 至今，湖南大学，人工智能与机器人学院，助理教授 / 副研究员。
• 2020.09 - 2025.06, Ph.D. in Mechanical Engineering, Huazhong University of Science and Technology. Advisor: Prof. Weiming Shen.2020.09 - 2025.06，华中科技大学，机械工程，博士，导师：沈卫明教授。
• 2023.10 - 2024.10, Visiting Ph.D. Researcher, Politecnico di Milano. Host: Prof. Giacomo Boracchi.2023.10 - 2024.10，米兰理工大学，计算机科学，访问博士，导师：Giacomo Boracchi。
• 2016.09 - 2020.06, B.E. in Mechanical Design, Manufacturing and Automation, Huazhong University of Science and Technology.2016.09 - 2020.06，华中科技大学，机械设计制造及其自动化，学士。

Openings and Mentoring招生与培养

I work directly with students on topic selection, research design, and paper writing. New members begin with a concrete research problem and learn how to complete a full project, including literature review, experiment design, implementation, and manuscript preparation. Undergraduate students are encouraged to start research early. Research assistant positions may be remote or on site, and a commitment of about one year is recommended for those seeking sustained research experience or preparing for further study.

我会直接参与选题、研究设计和论文指导。新同学入组后，通常先从一个具体课题做起，学习查阅文献、设计实验、实现算法和撰写论文。课题安排会结合个人基础和后续规划。本科生可提前进组；科研助理可线上或线下参与，建议连续投入一年左右，完成一个完整的研究项目。

I advised Yuhuan Du, an undergraduate from the 2023 cohort, on OmniPose-AD: Canonical Normal Rendering for Unaligned 3D Anomaly Detection. He is the first author, and I am the corresponding author. The paper received the Best Student Paper Award at ICAIS & ISAS 2026. Another student paper received the Best Student Paper Award at IEEE CSCWD 2025.

2023 级本科生杜禹寰以第一作者完成论文 OmniPose-AD: Canonical Normal Rendering for Unaligned 3D Anomaly Detection，获 ICAIS & ISAS 2026 Best Student Paper Award，本人为通讯作者。另有一篇学生论文获 IEEE CSCWD 2025 Best Student Paper Award。

The group collaborates with the University of Oxford, Politecnico di Milano, Tsinghua University, Huazhong University of Science and Technology, Huawei, Tencent Youtu Lab, CATL, and SEER Robotics. Students have access to computing resources, robotic platforms, research projects, paper-writing support, and academic or industrial collaboration opportunities.

课题组与牛津大学、米兰理工大学、清华大学、华中科技大学等高校保持合作，也与华为、腾讯优图、宁德时代、视比特机器人等企业开展联合研究。学生可使用团队的计算资源和机器人实验平台，并参与学术交流和企业合作项目。

Contact: Please send your CV to caoyunkang0207@gmail.com. Use the subject line “Master’s Application / Research Assistant Application - Name - University - Major - Expected Start Date.”

申请时请将个人简历发送至 caoyunkang0207@gmail.com，邮件标题请注明“硕士申请 / 科研助理申请 - 姓名 - 学校 - 专业 - 预计参与时间”。

Research Directions研究方向

The group studies four connected problems in industrial inspection: anomaly generation, anomaly detection, anomaly understanding, and embodied perception. Our work examines how inspection systems can learn from limited defect data, operate in open industrial environments, explain their findings, and guide robots to collect additional evidence and respond to anomalies.

我的研究主要包括以下四个方向，均面向工业检测，具体关注缺陷样本不足、未知异常识别、异常原因分析和机器人自主巡检等问题。

1. Anomaly Generation1. 异常生成

We study physics-informed generation of realistic industrial defects. Our work combines generative models with physical priors and explores foundation-model agents that can plan the generation process, assess sample quality, and refine results iteratively. The generated data support detector training, benchmarking, and long-tail anomaly analysis when real defects are scarce.

真实缺陷通常数量少、类型有限，采集和标注成本也较高。本方向研究引入物理先验的缺陷生成方法，使合成样本在外观和成因上更接近真实缺陷；同时探索大模型智能体在生成方案设计、样本筛选和自动迭代中的应用。生成数据主要用于检测模型训练、评测集构建和长尾异常分析。

2. Anomaly Detection2. 异常检测

We develop unsupervised, few-shot, zero-shot, and unified anomaly detection methods for industrial images, point clouds, 3D geometry, and multi-view data. The research covers foundation models, vision-language models, normal prototype modeling, fine-grained localization, and generalization across products, defect types, and production sites.

研究无监督、少样本、零样本和统一异常检测，数据形式包括 2D 图像、点云、3D 几何和多视角图像。重点关注正常原型建模、视觉语言模型、细粒度定位和跨产品泛化，希望模型在更换产线、产品或缺陷类型后仍能稳定使用。代表性成果包括首届 CVPR VAND 挑战赛全球亚军方法 Segment Any Anomaly，以及被多支获奖队伍采用的 INP-Former。

3. Anomaly Understanding3. 异常理解

We study multimodal anomaly understanding with foundation models. The research covers anomaly description, attribute recognition, cause analysis, visual question answering, risk assessment, and recovery suggestions. Representative work includes IAD-R1, which applies reinforcement learning to industrial anomaly reasoning.

传统异常检测通常只给出分数和热力图，难以直接回答异常是什么、为什么出现以及如何处理。本方向研究基于多模态大模型的异常描述、属性识别、原因分析、视觉问答、风险评估和恢复建议。代表性成果 IAD-R1 将强化学习用于工业异常推理。

4. Embodied Perception4. 具身感知

We integrate anomaly detection and understanding into robots and unmanned inspection systems. Robots actively select viewpoints, plan observation paths, gather multimodal evidence, and revisit suspicious regions. This allows them to discover, verify, and understand anomalies in open industrial environments and provide evidence for subsequent decisions and recovery actions.

具身感知面向机器人巡检。我们将异常检测和异常理解模型部署到机器人上，让机器人根据当前观测主动调整视角和路线，必要时返回疑似区域复查。这样，机器人可以在开放工业环境中完成异常发现、确认和解释，并将结果用于后续处置。

Representative Works代表性成果

The following works illustrate the current research line from anomaly generation and detection to understanding and embodied inspection.

以下列出几项与研究方向对应的代表性工作。

Anomaly Generation异常生成

Anomagic

Crossmodal prompt-driven zero-shot anomaly generation for controllable defect synthesis.

利用视觉和文本提示控制缺陷的位置与形态，在没有真实异常样本的情况下生成训练数据。

Repository

Anomaly Generation3D 缺陷合成

Synthesis4AD

A practical pipeline for 3D anomaly synthesis, model training, and online inference in industrial inspection.

面向 3D 工业检测，将缺陷合成、模型训练和在线推理组织为一套完整流程。

Repository

Anomaly Detection通用异常检测

INP-Former

Intrinsic normal prototypes extracted from a single image for universal anomaly detection.

从单张图像中提取正常原型，用于跨类别的通用异常检测。该方法被 CVPR VAND 多支获奖队伍采用。

Repository

Benchmark多视角多光照检测

M2AD

A large-scale benchmark for visual anomaly detection under coupled view and illumination changes.

针对视角和光照变化，构建多视角、多光照工业异常检测数据集，用于评估模型在复杂成像条件下的稳定性。

Project Page项目主页

Anomaly Understanding异常理解

IAD-R1

A post-training framework for industrial anomaly reasoning with vision-language models.

通过后训练提升视觉语言模型的异常推理能力，使其能够判断和定位异常，并说明判断原因。

Repository

3D Anomaly Detection点云异常检测

CPMF

Complementary pseudo multimodal features for point cloud anomaly detection.

结合 3D 点云和多视角 2D 特征，改进点云异常检测与细粒度定位。

Repository

Selected Research Projects部分科研项目

1. National Natural Science Foundation of China, Major Program Topic, Cross-species Multi-sensory and Multi-granularity Bionic Perception, 62595801, 2026/01 - 2030/12, ongoing, participant.国家自然科学基金委员会重大项目课题，跨物种多感官多粒度仿生感知，62595801，2026/01 - 2030/12，在研，参与。
2. Yuelushan Laboratory Seed Industry Special Project, Key Technologies and Applications for Crop Holographic Phenotype Acquisition and Analysis, YLS-20026-ZY01003, 2026/03 - 2028/03, ongoing, sub-project leader.岳麓山实验室种业专项，“人工智能+生物育种”技术攻关项目，作物全息表型采集与解析关键技术及应用，YLS-20026-ZY01003，2026/03 - 2028/03，在研，子课题负责人。
3. Fuyao University of Science and Technology, School of Intelligent Manufacturing and Future Technology Open Fund, Semi-supervised Industrial Image Anomaly Detection via Defect Generation, FIMFYUST-2025B05, 2025/07 - 2027/07, ongoing, principal investigator.福耀科技大学智造与未来技术学院开放基金，基于缺陷生成的半监督工业图像异常检测算法研究，FIMFYUST-2025B05，2025/07 - 2027/07，在研，主持。
4. Zhejiang University Hangzhou International Innovation Center entrusted project, AI Defect Sample Generation Algorithm Development, 2026/01 - 2026/12, ongoing, principal investigator.浙江大学杭州国际科创中心委托项目，AI 缺陷样本生成算法开发，2026/01 - 2026/12，在研，主持。
5. Fundamental Research Funds for the Central Universities, Foundation-model-driven Anomaly Detection, Reasoning, and Recovery, 2025/10 - 2030/10, ongoing, principal investigator.中央高校基本科研基金项目，基于基础模型驱动的异常检测、推理与修复技术研究，2025/10 - 2030/10，在研，主持。

Teaching开设课程

Undergraduate Courses本科生课程

• Mathematical Foundations of Artificial Intelligence, 32 hours人工智能中的数学基础，32 学时
• Circuit Experiments, 32 hours电路实验，32 学时
• Electronic Technology Practice II, 32 hours电子技术实践 II，32 学时

Graduate Courses研究生课程

• Philosophy and Ethics in Artificial Intelligence, 32 hours人工智能中的哲学与伦理，32 学时
• Robotics for the Future, 32 hours面向未来的机器人，32 学时

News最新动态

• 2026.07: I advised Wenzhuo Sun on an application to the National Undergraduate Innovation Training Program in a key support area. The application, “Zero-shot Industrial Anomaly Detection Based on Active Embodied Vision and a Digital Twin,” has been submitted internally at Hunan University.2026.07: 指导孙文卓申报国家级大学生创新训练计划重点支持领域项目“基于主动具身视觉与数字孪生的零样本工业异常检测关键技术研究”，项目已完成校内申报。
• 2026.06: I was elected Deputy Director of the Department of Robotics Engineering, School of Artificial Intelligence and Robotics, Hunan University.2026.06: 当选湖南大学人工智能与机器人学院机器人工程系副主任。
• 2026.05: Our paper “Cross-source Medical Anomaly Detection via Prompt-guided Diffusion Representations” has been accepted by Pattern Recognition.2026.05: 论文 “Cross-source Medical Anomaly Detection via Prompt-guided Diffusion Representations” 被 Pattern Recognition 录用。
• 2026.04: The Pattern Recognition Special Issue on Foundation Models for Anomaly Detection, Reasoning, and Recovery officially closed for submissions, receiving more than 230 manuscripts.2026.04: Pattern Recognition 特刊 “Foundation Models for Anomaly Detection, Reasoning, and Recovery” 正式截止投稿，累计收到 230 余篇稿件。
• 2026.03: Our paper “Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmark” has been accepted by Pattern Recognition.2026.03: 论文 “Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmark” 被 Pattern Recognition 录用。
• 2026.01: Our survey paper “A Comprehensive Survey for Real-World Industrial Defect Detection” has been accepted by Journal of Manufacturing Systems (JMS).2026.01: 综述论文 “A Comprehensive Survey for Real-World Industrial Defect Detection” 被 Journal of Manufacturing Systems 录用。
• 2025.12: Our paper on Zero-shot 3D Anomaly Detection has been accepted by IEEE TSMC.2025.12: 零样本 3D 异常检测论文被 IEEE TSMC 录用。
• 2025.11: Three papers have been accepted by AAAI 2026, including two oral presentations.2025.11: 3 篇论文被 AAAI 2026 录用，其中 2 篇为 Oral。
• 2025.09: I serve as the Executive Guest Editor for the Pattern Recognition Special Issue on Foundation Models for Anomaly Detection, Reasoning, and Recovery.2025.09: 担任 Pattern Recognition 特刊 “Foundation Models for Anomaly Detection, Reasoning, and Recovery” 执行客座编辑。
• 2025.05: Our student paper received the Best Student Paper Award at CSCWD 2025.2025.05: 指导学生论文获 CSCWD 2025 Best Student Paper Award。
• 2025.04: We organized the CVPR 2025 pre-conference “Industrial Vision” special session, attracting more than 5,000 online viewers.2025.04: 组织 CVPR 2025 预会议“工业视觉”专场，线上观看人数超过 5000。
• 2025.03: Two papers on unified anomaly detection and unseen anomaly generation have been accepted by CVPR 2025.2025.03: 关于统一异常检测和未见异常生成的 2 篇论文被 CVPR 2025 录用。

Representative Publications代表性论文

Note: * indicates equal contribution. † indicates corresponding author.

说明：* 表示共同第一作者，† 表示通讯作者。完整列表请见 Google Scholar.

Anomaly Generation异常生成

1. Sun H, Cao Y（曹云康）, Dong H, et al. Unseen Visual Anomaly Generation. IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025. doi:10.1109/CVPR52734.2025.02375. CCF-A.
2. Jiang Y, Luo W, Zhang H, Shen W, Cao Y†（曹云康）. Anomagic: Crossmodal Prompt-driven Zero-shot Anomaly Generation. AAAI Conference on Artificial Intelligence, 2026. doi:10.48550/arXiv.2511.10020. CCF-A.
3. Cheng Y, Cao Y（曹云康）, Wang D, et al. Boosting global-local feature matching via anomaly synthesis for multi-class point cloud anomaly detection. IEEE Transactions on Automation Science and Engineering, 22: 12560-12571, 2025. doi:10.1109/TASE.2025.3544462. 中科院二区.
4. Cao Y（曹云康）, Yao H, Cai Y, Zhang Y, Chen H, Zhang H, Shen W. Cross-source medical anomaly detection via prompt-guided diffusion representations. Pattern Recognition, 2026, 180(Part A): 113985. doi:10.1016/j.patcog.2026.113985.

Anomaly Detection异常检测

1. Cao Y（曹云康）, Zhang J, Frittoli L, et al. AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection. European Conference on Computer Vision, 2025. doi:10.1007/978-3-031-72761-0_4. CCF-B.
2. Luo W*, Cao Y*（曹云康）, Yao H, et al. Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection. IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025. doi:10.1109/CVPR52734.2025.00932. CCF-A.
3. Cao Y（曹云康）, Xu X, Cheng Y, et al. Personalizing Vision-Language Models with Hybrid Prompts for Zero-Shot Anomaly Detection. IEEE Transactions on Cybernetics, 55(4): 1917-1929, 2025. 中科院一区.
4. Cao Y（曹云康）, Xu X, Liu Z, et al. Collaborative discrepancy optimization for reliable image anomaly localization. IEEE Transactions on Industrial Informatics, 19(11): 10674-10683, 2023. 中科院一区.
5. Cao Y（曹云康）, Yao H, Luo W, et al. VarAD: Lightweight High-Resolution Image Anomaly Detection via Visual Autoregressive Modeling. IEEE Transactions on Industrial Informatics, 21(4): 3246-3255, 2025. 中科院一区，高被引论文.
6. Cao Y（曹云康）, Xu X, Shen W. Complementary pseudo multimodal feature for point cloud anomaly detection. Pattern Recognition, 156: 110761, 2024. doi:10.1016/j.patcog.2024.110761. 中科院一区.
7. Cheng Y*, Cao Y*（曹云康）, Xie G, et al. Towards zero-shot point cloud anomaly detection: A multi-view projection framework. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(3): 1747-1760, 2026. doi:10.1109/TSMC.2025.3648581. 中科院一区.
8. Cao Y（曹云康）, Cheng Y, Zhang Y, et al. Visual anomaly detection under complex view-illumination interplay: A large-scale benchmark. Pattern Recognition, 2026.

Anomaly Understanding异常理解

1. Li Y, Cao Y（曹云康）, Liu C, et al. IAD-R1: Reinforcing Consistent Reasoning in Industrial Anomaly Detection. AAAI Conference on Artificial Intelligence, 2026. doi:10.48550/arXiv.2508.09178. CCF-A, Oral.
2. Xu X, Cao Y（曹云康）, Zhang H, Sang N, Huang X. Customizing Visual-Language Foundation Models for Multi-Modal Anomaly Detection and Reasoning. International Conference on Computer Supported Cooperative Work in Design, 2025. CCF-C, Best Student Paper Award.
3. Zhang Y, Cao Y（曹云康）, Xu X, et al. LogiCode: An LLM-Driven Framework for Logical Anomaly Detection. IEEE Transactions on Automation Science and Engineering, 22: 7712-7723, 2025. 中科院二区.
4. Cai W, Huang W, Cao Y（曹云康）, et al. Towards VLM-based Hybrid Explainable Prompt Enhancement for Zero-Shot Industrial Anomaly Detection. International Joint Conference on Artificial Intelligence, 2025. CCF-A.

Embodied Perception具身感知

1. Liu J*, Cao Y*（曹云康）, Chen Y*, Li C, Du Y, Zhang H. Towards Active Real-to-Twin Inspection: A New Paradigm for Zero-Shot Anomaly Detection. The 16th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, 2026. arXiv:2605.25407.
2. Du Y, Zhang H, Cheng Y, Huang C, Cao Y†（曹云康）. OmniPose-AD: Canonical Normal Rendering for Unaligned 3D Anomaly Detection. 2026 Joint International Conference on Automation-Intelligence-Safety and International Symposium on Autonomous Systems, 2026: 1-6. doi:10.1109/ICAISISAS68969.2026.11567774. Best Student Paper.
3. Cheng Y, Sun Y, Zhang H, Shen W, Cao Y†（曹云康）. Towards high-resolution 3D anomaly detection: A scalable dataset and real-time framework for subtle industrial defects. AAAI Conference on Artificial Intelligence, 2026. doi:10.48550/arXiv.2507.07435. CCF-A, Oral.
4. Zhang H, Liu H, Biekezati B, Cao Y（曹云康）, et al. FPF: A Focused Perception Framework for Small Defect Identification in Complex Power Scenarios. IEEE Transactions on Industrial Informatics, doi:10.1109/TII.2025.3649024, 2026. 中科院一区.

Selected Authorized Patents代表性授权专利

1. 张辉，杜瑞，别克扎提·巴合提，陈厚权，邱宇，张恺宁，曹云康，王耀南. 一种基于霍奇分解与多模态融合的部件分割方法及系统：中国，ZL202511195689.2，2025年10月31日，授权。
2. 张辉，唐友源，杜瑞，别克扎提·巴合提，陈厚权，张恺宁，曹云康，邱宇，王耀南. 一种基于结构感知框架的架空电力线覆冰厚度检测方法和系统：中国，ZL202511195907.2，2025年10月31日，授权。
3. 沈卫明，程育奇，曹云康，张以恒，孙依晗，谭宇翔，张雨昕. 一种复杂零件缺陷数据标注方法、缺陷检测方法及多视角多光照数据采集装置：中国，ZL202510060769.0，2025年12月2日，授权。
4. 沈卫明，程育奇，曹云康. 一种考虑原型分数校正的点云异常检测方法及设备：中国，ZL202510040267.1，2026年2月17日，授权。
5. 沈卫明，程育奇，曹云康. 一种点云数据局部异常生成方法及系统：中国，ZL202410633098.8，2025年2月11日，授权。
6. 沈卫明，程育奇，曹云康. 一种考虑多层级特征的多类别点云异常检测方法及系统：中国，ZL202410622146.3，2025年2月11日，授权。
7. 沈卫明，程育奇，曹云康. 一种考虑提示学习的零样本点云异常检测方法及系统：中国，ZL202410359413.2，2024年11月5日，授权。
8. 沈卫明，姜雨欣，曹云康. 基于原型学习引导的判别分割网络的小样本缺陷检测方法：中国，ZL202311254405.3，2025年11月4日，授权。
9. 沈卫明，刘照阁，徐晓豪，曹云康. 基于像素单点及多元配对的无监督异常检测方法：中国，ZL202310570510.1，2026年1月6日，授权。
10. 沈卫明，姜雨欣，曹云康. 一种工业缺陷检测方法及系统：中国，ZL202310570502.7，2025年11月21日，授权。

Awards科研获奖经历

1. Key Technologies and Applications of Multimodal Perception and Collaborative Optimization for Collaborative Intelligent Manufacturing, China Association of Inventions Invention Entrepreneurship Award, Project Award Second Prize, 3rd ranked, Dec. 2025.面向协同智能制造的多模态感知与协同优化关键技术及应用，中国发明协会发明创业奖项目奖二等奖，排名第三，2025年12月。
2. Key Technologies and Applications of Multimodal Perception and Collaborative Optimization for Collaborative Intelligent Manufacturing, Gold Award of the 29th National Invention Exhibition, 3rd ranked, Oct. 2025.面向协同智能制造的多模态感知与协同优化关键技术及应用，第二十九届全国发明展览会金奖，排名第三，2025年10月。
3. Yunkang Cao, Xiaohao Xu, Chen Sun, Yuqi Cheng, Liang Gao, Weiming Shen. Runner-up, CVPR Visual Anomaly and Novelty Detection Challenge, Jun. 2023.Yunkang Cao, Xiaohao Xu, Chen Sun, Yuqi Cheng, Liang Gao, Weiming Shen. CVPR Visual Anomaly and Novelty Detection Challenge，全球亚军，2023年6月。
4. Xiaohao Xu, Yunkang Cao, Huaxin Zhang, Nong Sang, Xiaonan Huang. Best Student Paper Award, IEEE Computer Supported Cooperative Work in Design, May 2025.Xiaohao Xu, Yunkang Cao, Huaxin Zhang, Nong Sang, Xiaonan Huang. IEEE Computer Supported Cooperative Work in Design，Best Student Paper Award，2025年5月。
5. Yuhuan Du et al. OmniPose-AD: Canonical Normal Rendering for Unaligned 3D Anomaly Detection, Best Student Paper Award, ICAIS & ISAS, 2026. Yuhuan Du is the student first author; Yunkang Cao is the corresponding author.杜禹寰等，OmniPose-AD: Canonical Normal Rendering for Unaligned 3D Anomaly Detection，ICAIS & ISAS 2026 Best Student Paper Award。杜禹寰为学生第一作者，曹云康为通讯作者。
6. Yunkang Cao, National Scholarship for Ph.D. Students, Nov. 2024.曹云康，博士研究生国家奖学金，2024年11月。

Academic Service学术服务

Editorial and Reviewing Service编委与审稿服务

• Editorial Board Member, Pattern Recognition.Pattern Recognition 编委。
• Lead organizer of the Special Issue on “Foundation Models for Anomaly Detection, Reasoning, and Recovery.”牵头组织“面向缺陷检测、推理与修复的基础模型”专题特刊。
• Special Session Chair, IEEE CSCWD 2025.IEEE CSCWD 2025 专题主席。
• Reviewer for TPAMI, IJCV, CVPR, ICCV, NeurIPS, AAAI, IJCAI, Pattern Recognition, IEEE TCYB, IEEE TII, and other journals and conferences.担任 TPAMI、IJCV、CVPR、ICCV、NeurIPS、AAAI、IJCAI、Pattern Recognition、IEEE TCYB、IEEE TII 等期刊与会议审稿人。

Workshop and Forum Organization研讨会与论坛组织

1. CVPR 2024-2026, Visual Anomaly and Novelty Detection Workshop (VAND).CVPR 2024-2026，视觉异常与新颖性检测研讨会 VAND。
2. IJCAI 2024, Anomaly Detection with Foundation Models Workshop (ADFM).IJCAI 2024，基于基础模型的异常检测研讨会 ADFM。
3. ICCV 2025, Anomaly Detection with Foundation Models Workshop (ADFM).ICCV 2025，基于基础模型的异常检测研讨会 ADFM。
4. CVPR 2026, Anomaly Detection with Foundation Models Workshop (ADFM).CVPR 2026，基于基础模型的异常检测研讨会 ADFM。
5. IEEE CASE, Special Session on Industrial Foundation Models and Applications in Smart Manufacturing.IEEE CASE，“智能制造中的工业大模型及其应用”专题。
6. CSIG Donghu Forum, CVPR 2025 pre-conference "Industrial Vision" special session.CSIG “东湖论坛”前沿论文分享会 CVPR 2025 预会议“工业视觉”专场。
7. YAC 2026, Special Session on Industrial Vision Intelligent Measurement and Inspection, Special Session Chair, Changsha.YAC 2026，“工业视觉智能测量与检测”专题，专题主席，长沙。
8. The 3rd International Conference on 3D Vision, Perception and Applications, Robot Intelligent Inspection Forum, Forum Secretary, Suzhou.第三届国际 3D 视觉感知与应用大会，“机器人智能检测”分会，论坛秘书，苏州。
9. CSIG Frontier Forum on Embodied Intelligent Perception and Inspection, Organizing Committee Chair, Guilin.CSIG 具身智能感知与检测前沿论坛，组织委员会主席，桂林。