MPCM-Net: Multi-scale network integrates partial attention convolution with Mamba for ground-based cloud image segmentation
School of Artificial Intelligence, Hebei University of Technology（河北工业大学人工智能学院）；School of Electrical Engineering, Hebei University of Technology（河北工业大学电气工程学院）；University of Southern Queensland（南方昆士兰大学）；Hebei Province Key Laboratory of Big Data Calculation, Hebei University of Technology（河北省大数据计算重点实验室）；Discipline of Business Systems and Operations, School of Business and Law, Edith Cowan University（商学院与法律学院商业系统与运营学科，埃德温·科温大学）
Ground-based cloud image segmentation is a critical research domain for photovoltaic power forecasting. Current deep learning approaches primarily focus on encoder-decoder architectural refinements. However, existing methodologies exhibit several limitations:(1)they rely on dilated convolutions for multi-scale context extraction, lacking the partial feature effectiveness and interoperability of inter-channel;(2)attention-based feature enhancement implementations neglect accuracy-throughput balance; and (3)the decoder modifications fail to establish global interdependencies among hierarchical local features, limiting inference efficiency. To address these challenges, we propose MPCM-Net, a Multi-scale network that integrates Partial attention Convolutions with Mamba architectures to enhance segmentation accuracy and computational efficiency. Specifically, the encoder incorporates MPAC, which comprises:(1)a MPC block with ParCM and ParSM that enables global spatial interaction across multi-scale cloud formations, and (2)a MPA block combining ParAM and ParSM to extract discriminative features with reduced computational complexity. On the decoder side, a M2B is employed to mitigate contextual loss through a SSHD that maintains linear complexity while enabling deep feature aggregation across spatial and scale dimensions. As a key contribution to the community, we also introduce and release a dataset CSRC, which is a clear-label, fine-grained segmentation benchmark designed to overcome the critical limitations of existing public datasets. Extensive experiments on CSRC demonstrate the superior performance of MPCM-Net over state-of-the-art methods, achieving an optimal balance between segmentation accuracy and inference speed. The dataset and source code will be available at https://github.com/she1110/CSRC.
CAMS: Convolution and Attention-Free Mamba-based Cardiac Image Segmentation
School of Electronic Engineering and Computer Science, Queen Mary University of London（伦敦玛丽女王大学电子工程与计算机科学学院）；Queen Mary’s Digital Environment Research Institute (DERI)（伦敦玛丽女王大学数字环境研究所）；Department of Computer Science, University College London（伦敦大学学院计算机科学系）；School of Engineering and Materials Science, Queen Mary University of London（伦敦玛丽女王大学工程与材料科学学院）
Beyond Mamba: Enhancing State-space Models with Deformable Dilated Convolutions for Multi-scale Traffic Object Detection
Department of Physics, J.K. Institute of Science（J.K.科学研究所物理系）；World Scientific University（世界科学出版社大学）；University of Intelligent Studies（智能研究大学）；School of Computer and Electronic Information, Nanjing Normal University（南京师范大学计算机与电子信息学院）
MambaPanoptic: A Vision Mamba-based Structured State Space Framework for Panoptic Segmentation
Technical University of Munich（慕尼黑技术大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Polytechnic University of Milan（米兰理工大学）；University of Stuttgart（斯图加特大学）；Wuhan University（武汉大学）；Karlsruhe University of Applied Sciences（卡尔斯鲁厄应用科学大学）
Mamba-Driven Topology Fusion for Monocular 3D Human Pose Estimation
College of Sciences, Northeastern University（东北大学理学院）；Key Laboratory of Differential Equations and Their Applications, Northeastern University（东北大学偏微分方程及其应用重点实验室）；Nanjing University of Science and Technology（南京理工大学）
U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation
Peter Munk Cardiac Centre, University Health Network（彼得·蒙克心脏中心，大学健康网络）；University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）
Mamba, a State Space Model (SSM), has recently shown competitive performance to Convolutional Neural Networks (CNNs) and Transformers in Natural Language Processing and general sequence modeling. Various attempts have been made to adapt Mamba to Computer Vision tasks, including medical image segmentation (MIS). Vision Mamba (VM)-based networks are particularly attractive due to their ability to achieve global receptive fields, similar to Vision Transformers, while also maintaining linear complexity in the number of tokens. However, the existing VM models still struggle to maintain both spatial…
MS-UMamba: An Improved Vision Mamba Unet for Fetal Abdominal Medical Image Segmentation
Guangxi Key Laboratory of Machine Vision and Intelligent Control（广西机器视觉与智能控制重点实验室）；Wuzhou University（梧州大学）；College of Electronical and Information Engineering（电子信息工程学院）；Wuzhou Medical College（梧州医学院）；School of Microelectronics（微电子学院）；Southern Medical University（南方医科大学）
HyM-UNet: Synergizing Local Texture and Global Context via Hybrid CNN-Mamba Architecture for Medical Image Segmentation
School of Information and Communication Engineering, Beijing University of Posts and Communications (BUPT)（信息与通信工程学院，北京邮电大学）；Alibaba Cloud AI Labs（阿里云人工智能实验室）
Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院保罗·C·劳特伯生物医学成像研究中心）；Peng Cheng Laboratory（鹏城实验室）；University of Chinese Academy of Sciences（中国科学院大学）；The University of Hong Kong（香港大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image Segmentation
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Institute of Wenzhou, Zhejiang University（浙江大学温州研究院）；The Second Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院附属第二医院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Notre Dame（圣母大学）
Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook
School of Advanced Technology, Xi’an Jiaotong-Liverpool University（西安交通大学利物浦大学先进技术学院）；School of Electronics and Information Engineering, Beihang University（北航电子信息工程学院）；Department of Paediatrics, Cambridge University（剑桥大学儿科部）；School of Computing and Mathematical Sciences, University of Leicester（莱斯特大学计算与数学科学学院）；Department of Computing Science, Robert Gordon University（罗伯特·戈登大学计算机科学系）；National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别国家实验室）；Nanyang Technological University, Singapore（新加坡南洋理工大学）；Department of Computer Science, University of Liverpool（利物浦大学计算机科学系）
School of Advanced Technology, Xi’an Jiaotong-Liverpool University（西交利物浦大学先进技术学院）；School of Electronics and Information Engineering, Beihang University（北京航空航天大学电子信息工程学院）；Cambridge University（剑桥大学）；Beijing Shijitan Hospital, Capital Medical University（首都医科大学附属北京世纪坛医院）；University of Liverpool（利物浦大学）
The University of Tokyo（东京大学）；Wuhan University（武汉大学）；RIKEN（理化学研究所）；Microsoft Research Asia（微软亚洲研究院）
Convolutional neural networks (CNN) and Transformers have made impressive progress in the field of remote sensing change detection (CD). However, both architectures have inherent shortcomings: CNN are constrained by a limited receptive field that may hinder their ability to capture broader spatial contexts, while Transformers are computationally intensive, making them costly to train and deploy on large datasets. Recently, the Mamba architecture, based on state space models, has shown remarkable performance in a series of natural language processing tasks, which can effectively compensate for …
MedMamba: Vision Mamba for Medical Image Classification
School of Biomedical Engineering（生物医学工程学院）；Guangzhou Medical University（广州医科大学）；School of Mathematics and Systems Science（数学与系统科学学院）；Guangdong Polytechnic Normal University（广东技术师范大学）
Graph Mamba Survival Analysis Based on Topology-Aware ordering
School of Mathematics and Statistics（数学与统计学学院）；West China Science and Technology Innovation Harbor（西部科学与技术创新港）；School of Computer Science and Technology（计算机科学与技术学院）
RadioMamba: Breaking the Accuracy-Efficiency Trade-off in Radio Map Construction via a Hybrid Mamba-UNet
State Key Laboratory of ISN and School of Telecommunications Engineering, Xidian University（信息与通信工程国家重点实验室和西安电子科技大学电信工程学院）；Department of Electrical and Computer Engineering, University of Waterloo（滑铁卢大学电气与计算机工程系）
School of Computer Science and Technology, Xinjiang University（新疆大学计算机科学与技术学院）；Joint Research Laboratory for Embodied Intelligence, Xinjiang University（新疆大学具身智能联合研究实验室）；Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing, Xinjiang University（新疆大学丝绸之路多语言认知计算国际联合研究实验室）
Attention Mamba: Time Series Modeling with Adaptive Pooling Acceleration and Receptive Field Enhancements
Graduate School of Information Science and Electrical Engineering, Kyushu University（九州大学情报科学与电气工程研究院）；Graduate School of Business, ECUST, East China University of Science and Technology（华东理工大学商学院）
