When training samples are scarce, the semantic embedding technique, ie, describing class labels with attributes, provides a condition to generate visual features for unseen objects by transferring the knowledge from seen objects. However, semantic descriptions are usually obtained in an external paradigm, such as manual annotation, resulting in weak consistency between descriptions and visual features. In this paper, we refine the coarse-grained semantic description for any-shot learning tasks, ie, zero-shot learning (ZSL), generalized zero-shot learning (GZSL), and few-shot learning (FSL). A new model, namely, the semantic refinement Wasserstein generative adversarial network (SRWGAN) model, is designed with the proposed multihead representation and hierarchical alignment techniques. Unlike conventional methods, semantic refinement is performed with the aim of identifying a bias-eliminated condition for disjoint-class feature generation and is applicable in both inductive and transductive settings. We extensively evaluate model performance on six benchmark datasets and observe state-of-the-art results for any-shot learning; eg, we obtain 70.2% harmonic accuracy for the Caltech UCSD Birds (CUB) dataset and 82.2% harmonic accuracy for the Oxford Flowers (FLO) dataset in the standard GZSL setting. Various visualizations are also provided to show the bias-eliminated generation of SRWGAN. Our code is available.
RUFNet: Query-Guided Support Mask Refinement and Uncertainty Fusion based on Hybrid Mamba for Few-Shot Brain Tumor Segmentation
Department of Language Science and Technology, The Hong Kong Polytechnic University（香港理工大学语言科学与技术系）；School of Artificial Intelligence, Chongqing University of Technology（重庆理工大学人工智能学院）；Affiliated Lianyungang Hospital of Xuzhou Medical University（徐州医科大学附属连云港医院）；College of Mechatronics and Control Engineering, Shenzhen University（深圳大学机电与控制工程学院）
ANYPORTAL: Zero-Shot Consistent Video Background Replacement
Wangxuan Institute of Computer Technology, State Key Laboratory of Multimedia Information Processing, Peking University, Beijing, China（王轩计算机技术研究所，多媒体信息处理国家重点实验室，北京大学，北京）
FREE: Feature Refinement for Generalized Zero-Shot Learning
Huazhong University of Science and Technology (HUST)（华中科技大学）；Southern University of Science and Technology (SUSTech)（南方科技大学）；Inception Institute of Artificial Intelligence (IIAI)（起源人工智能研究院）
Judging from Support-set: A New Way to Utilize Few-Shot Segmentation for Segmentation Refinement Process
Brookhaven National Laboratory（布鲁赫斯国家实验室）；Roblox（Roblox公司）；Rutgers University（罗格斯大学）；Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Single-Shot Refinement Neural Network for Object Detection
CBSR & NLPR, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别国家重点实验室与北方激光技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；GE Global Research（通用电气全球研究院）
Global Prompt Refinement with Non-Interfering Attention Masking for One-Shot Federated Learning
School of Software, Shandong University（山东大学软件学院）；AIM Lab, Faculty of Engineering, Monash University（莫纳什大学工程学院AIM实验室）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Department of Electrical and Computer Engineering, University of British Columbia（不列颠哥伦比亚大学电气与计算机工程系）；Vector Institute, Canada（加拿大向量研究所）
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）
Multi-Granularity Mutual Refinement Network for Zero-Shot Learning
School of Computer Science and Technology, Xidian University（电子科技大学计算机科学与技术学院）；Donghai Laboratory（东海实验室）；Edith Cowan University（埃迪斯·科文大学）；University of Western Australia（西澳大学）
Inter- and Intra-image Refinement for Few Shot Segmentation
Department of Biomedical Engineering, Peking University（北京大学生物医学工程系）；Institute of Medical Technology, Peking University Health Science Center, Peking University（北京大学医学部医学技术研究所）；National Biomedical Imaging Center, Peking University（北京大学国家生物医学成像中心）
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）
GReFEM: Multimodal LLMs as Zero-Shot Semantic Assistants for Physics-Guided 3D Mesh Refinement
Institute of Material Systems Modeling（材料系统建模研究所）；Helmholtz Zentrum Hereon（海德堡研究中心Hereon）；Institute of Material and Process Design（材料与加工设计研究所）；German Research Center for Artificial Intelligence（德国人工智能研究中心）
Enhancing Few-Shot Out-of-Distribution Detection via the Refinement of Foreground and Background
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；School of Computer Science and Technology, Hainan University（海南大学计算机科学与技术学院）
University of Strasbourg（斯特拉斯堡大学）；CNRS（法国国家科学研究中心）；INSERM（法国国家健康与医学研究院）；ICube（ICube研究中心）；UMR7357（法国大学-研究中心7357）；IHU Strasbourg（斯特拉斯堡IHU医院）；University Digestive Health Care Center – Clarunis（大学消化健康中心 – Clarunis）；Ludwig Maximilian University of Munich（慕尼黑路易斯·马克西米利安大学）；Chinese University of Hong Kong（香港中文大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）
FoRIS: Progressive Foreground Refinement for Training-Free In-Context Segmentation
Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences（中国科学院西安光学精密机械研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Xi’an Jiaotong University（西安交通大学）；Zhongnan Hospital of Wuhan University（武汉大学中南医院）
