Semantic segmentation methods can not directly identify abnormal objects in images. Anomaly Segmentation algorithm from this realistic setting can distinguish between in-distribution objects and Out-Of-Distribution (OOD) objects and output the anomaly probability for pixels. In this paper, a Prototype Guided Anomaly segmentation Network (PGAN) is proposed to extract semantic prototypes for in-distribution training data from limited annotated images. In the model, prototypes are used to model the hierarchical category semantic information and distinguish OOD pixels. The proposed PGAN model includes a semantic segmentation network and a prototype extraction network. Similarity measures are adopted to optimize the prototypes. The learned semantic prototypes are used as category semantics to compare the similarity with features extracted from test images and then to generate semantic segmentation prediction. The proposed prototype extraction network can also be integrated into most semantic segmentation networks and recognize OOD pixels. On the StreetHazards dataset, the proposed PGAN model produced mIoU of 53.4% for anomaly segmentation. The experimental results demonstrate PGAN may achieve the SOTA performance in the anomaly segmentation tasks.
SPENet: Self-guided Prototype Enhancement Network for Few-shot Medical Image Segmentation
School of Computer Science, Beijing University of Technology, Beijing, China（北京理工大学计算机科学学院）；Institute of Automation Chinese Academy of Sciences, Beijing, China（中国科学院自动化研究所）；Multimedia Department Xiaomi Inc, Beijing, China（小米公司多媒体部门）；Capital Medical University Affiliated Beijing Friendship Hospital, Beijing, China（首都医科大学附属北京友谊医院）
DualProtoSeg: Simple and Efficient Design with Text- and Image-Guided Prototype Learning for Weakly Supervised Histopathology Image Segmentation
University of Houston（德克萨斯大学休斯顿分校）；Ho Chi Minh City University of Technology（胡志明市技术大学）；University of Information Technology（信息技术大学）；University of Technology Sydney（悉尼技术大学）；Vin University（文大学）；Department of Computer Science, Emory University（埃默里大学计算机科学系）；College of Medical Science and Technology, Taipei Medical University（台北医学院医学科学与技术学院）；Montefiore Medical Center, Albert Einstein College of Medicine（蒙特福医院，阿尔伯特·爱因斯坦医学院）；School of Computer Science, Carnegie Mellon University（卡内基梅隆大学计算机科学学院）
Diverse Normal Prototypes-Guided Contrastive Reconstruction for Medical Anomaly Detection
School of Mathematics, Shandong University（山东大学数学学院）；Department of Interventional Medicine and Minimally Invasive Oncology, The Second Qilu Hospital of Shandong University（山东大学第二齐鲁医院介入医学与微创肿瘤科）；Department of Neurosurgery, The Second Qilu Hospital of Shandong University（山东大学第二齐鲁医院神经外科）
Physics-inspired Pseudo Anomaly Generation and Prototype Feature Guidance for 3D Anomaly Detection
School of Computer Science, Wuhan University（武汉大学计算机科学学院）；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））；School of Information and Software Engineering, East China Jiaotong University（华东交通大学信息工程学院）；School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）
BoRAD: Bootstrap your Own Representations for Multi-class Anomaly Detection
Department of Artificial Intelligence, FPT University（FPT大学人工智能系）；Department of IT, FPT University（FPT大学信息技术系）；Department of Computing Fundamental, FPT University（FPT大学计算基础系）
Distribution Aligned Diffusion and Prototype-guided network for Unsupervised Domain Adaptive Segmentation
Xi’an Jiaotong University（西安交通大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of California, Santa Cruz（加州大学圣克鲁兹分校）
Key Laboratory of Multimedia Trusted Perception and Efficient Computing（多媒体可信感知与高效计算重点实验室）；Ministry of Education of China（中华人民共和国教育部）；Xiamen University（厦门大学）；VIVO
Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptation
Peking University（北京大学）；South China University of Technology（华南理工大学）；National University of Singapore（新加坡国立大学）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）
Query-guided Prototype Evolution Network for Few-Shot Segmentation
Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）；School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）；School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）
Prototype-Guided Curriculum Learning for Zero-Shot Learning
School of Electronic Information and Communications, Huazhong University of Science and Technology（电子信息与通信学院，华中科技大学）；Mohamed bin Zayed University of AI（马尔代夫人工智能大学）；School of Computer Science and Engineering, Nanjing University of Science and Technology（计算机科学与工程学院，南京理工大学）；School of Computer Science, The University of Sydney（计算机科学学院，悉尼大学）
School of Computer Science & Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；School of Computing and Information Technology, Great Bay University（海湾大学计算机与信息学院）
Reliability-Aware Prototype Calibration for Frozen Pose-Flow Video Anomaly Detection
School of Information Engineering（信息工程学院）；Suqian University（宿迁大学）；School of Electronic & Information Engineering（电子与信息工程学院）；Nanjing University of Information Science and Technology（南京信息科学技术大学）；University of Electronic Science and Technology of China（电子科学与技术大学）；Institute for Advanced Studies（高级研究机构）；Universiti Malaya（马来亚大学）
SEGA: Semantic Guided Attention on Visual Prototype for Few-Shot Learning
Institute of Computing Technology, CAS（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）；Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）
Semantically Guided Dynamic Visual Prototype Refinement for Compositional Zero-Shot Learning
National Key Laboratory of Radar Signal Processing, Xidian University（雷达信号处理国家级重点实验室，西安电子科技大学）；Research Institute of Systems Engineering, Academy of Military Science（系统工程研究所，军事科学院）
Towards Explainable Industrial Anomaly Detection via Knowledge-Guided Latent Reasoning
School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区计算机科学与技术学院）；School of Artificial Intelligence and Robotics, Hunan University（湖南大学人工智能与机器人学院）；Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）
