Hyperspectral images (HSIs) have great potential in various visual tasks due to their rich spectral information. However, obtaining high-resolution hyperspectral images remains challenging due to limitations of physical imaging. Inspired by Kolmogorov-Arnold Networks (KANs), we propose an efficient HSI super-resolution (HSI-SR) model to fuse a low-resolution HSI (LR-HSI) and a high-resolution multispectral image (HR-MSI), yielding a high-resolution HSI (HR-HSI). To achieve the effective integration of spatial information from HR-MSI, we design a fusion module based on KANs, called KAN-Fusion. Further inspired by the channel attention mechanism, we design a spectral channel attention module called KAN Channel Attention Block (KAN-CAB) for post-fusion feature extraction. As a channel attention module integrated with KANs, KAN-CAB not only enhances the fine-grained adjustment ability of deep networks, enabling networks to accurately simulate details of spectral sequences and spatial textures, but also effectively avoid Curse of Dimensionality. Extensive experiments show that, compared to current state-of-the-art HSI-SR methods, proposed HSR-KAN achieves the best performance in terms of both qualitative and quantitative assessments. Our code is available at: https://github.com/Baisonm-Li/HSR-KAN.
KANO: Kolmogorov-Arnold Neural Operator for Image Super-Resolution
Southeast University（东南大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航空信息研究所）；College of Resources and Environment, University of Chinese Academy of Sciences（中国科学院大学资源与环境学院）；Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK（格勒诺布尔阿尔卑斯大学、法国国家科研机构（Inria）、法国国家科学研究中心（CNRS）、格勒诺布尔INP、LJK）
Vision KAN: Towards an Attention-Free Backbone for Vision with Kolmogorov-Arnold Networks
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China（深圳大学计算机科学与软件工程学院）；School of Computer Science, University of Nottingham Ningbo China, Ningbo, China（诺丁汉大学宁波校区计算机科学学院）；School of Artificial Intelligence, Shenzhen University, Shenzhen, China（深圳大学人工智能学院）
KANICE: Kolmogorov-Arnold Networks with Interactive Convolutional Elements
the University of New Orleans（新奥尔良大学）；Naval Research Laboratory（美国海军研究实验室）；US Army Corps of Engineers, Engineer Research and Development Center（美国陆军工程兵团工程研究与发展中心）
KANResDiff: Learning Local Residual Diffusion via Kolmogorov-Arnold Network for Ambiguous Medical Image Segmentation
Faculty of Computing, Harbin Institute of Technology（哈尔滨工业大学计算学部）；College of Computer and Control Engineering, Northeast Forestry University（东北林业大学计算机与控制工程学院）；Case Western Reserve University（凯斯西储大学）
DE-KAN: A Kolmogorov Arnold Network with Dual Encoder for accurate 2D Teeth Segmentation
School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；National Engineering Research Center of Oral Biomaterials and Digital Medical Devices（口腔生物材料与数字医疗设备国家工程研究中心）；Department of Statistics and Data Science, Jahangirnagar University（Jahangirnagar大学统计与数据科学系）；Faculty of Information Technology, Beijing University of Technology（北京理工大学信息学院）；Department of Orthodontics, Peking University School and Hospital of Stomatology, National Center for Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Research Center of Oral Biomaterials and Digital Medical Devices（北京大学口腔医学院附属口腔医院、口腔疾病国家临床医学研究中心、口腔生物材料与数字医疗设备国家工程研究中心）
DE-KAN 通过双编码器 Kolmogorov Arnold 网络实现高精度 2D 牙齿分割，实验显示其在 Dice 系数上比现有方法提升达 4.7%。
KANGURA: Kolmogorov-Arnold Network-Based Geometry-Aware Learning with Unified Representation Attention for 3D Modeling of Complex Structures
Department of Electrical Engineering, Iran University of Science and Technology（伊朗科学技术大学电气工程系）；Department of Computer Engineering, Iran University of Science and Technology（伊朗科学技术大学计算机工程系）；Department of Industrial & Management Systems Engineering, West Virginia University（西弗吉尼亚大学工业与管理系统工程系）；H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology（佐治亚理工学院H.米尔顿·斯图尔特工业与系统工程学院）；Department of Computer Science, University of California, Davis（加州大学戴维斯分校计算机科学系）
QuKAN: A Quantum Circuit Born Machine approach to Quantum Kolmogorov Arnold Networks
Department of Computer Science and Research Initiative QC-AI, RPTU Kaiserslautern-Landau（计算机科学系和QC-AI研究计划，鲁尔-帕绍大学（RPTU）凯斯贝赫-劳恩道分校）；Embedded Intelligence, German Research Center for Artificial Intelligence (DFKI)（嵌入式智能，德国人工智能研究中心（DFKI））；Department of Physics, RPTU Kaiserslautern-Landau（物理系，鲁尔-帕绍大学（RPTU）凯斯贝赫-劳恩道分校）
本研究利用量子电路 Born 机（QCBM）在混合与全量子形式下实现了 KAN 架构，通过预训练残差函数发挥参数化量子电路的表示能力，验证了 QuKAN 架构的可行性、可解释性与性能。
KAN Text to Vision? The Exploration of Kolmogorov-Arnold Networks for Multi-Scale Sequence-Based Pose Animation from Sign Language Notation
School of Computer Science, The University of Sydney（悉尼大学计算机科学学院）；School of Science, Edith Cowan University（埃迪斯科文大学科学学院）；School of Computer Science and Digital Technologies, Aston University（阿斯顿大学计算机科学与数字技术学院）
