Attention-based methods have demonstrated exceptional performance in modelling long-range dependencies on spherical cortical surfaces, surpassing traditional Geometric Deep Learning (GDL) models. However, their extensive inference time and high memory demands pose challenges for application to large datasets with limited computing resources. Inspired by the state space model in computer vision, we introduce the attention-free Vision Mamba (Vim) to spherical surfaces, presenting a domain-agnostic architecture for analyzing data on spherical manifolds. Our method achieves surface patching by representing spherical data as a sequence of triangular patches derived from a subdivided icosphere. The proposed Surface Vision Mamba (SiM) is evaluated on multiple neurodevelopmental phenotype regression tasks using cortical surface metrics from neonatal brains. Experimental results demonstrate that SiM outperforms both attention- and GDL-based methods, delivering 4.8 times faster inference and achieving 91.7% lower memory consumption compared to the Surface Vision Transformer (SiT) under the Ico-4 grid partitioning. Sensitivity analysis further underscores the potential of SiM to identify subtle cognitive developmental patterns. The code is available at https://github.com/Rongzhao-He/surface-vision-mamba.
Enhancing Mamba Decoder with Bidirectional Interaction in Multi-Task Dense Prediction
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence（国家人类-机器混合增强智能重点实验室）；National Engineering Research Center for Visual Information and Applications（国家视觉信息与应用工程技术研究中心）；Institute of Artificial Intelligence and Robotics（人工智能与机器人研究院）；School of Computing and Artificial Intelligence（计算与人工智能学院）；School of Electronic and Information Engineering（电子与信息工程学院）
BiSegMamba: Efficient Bidirectional Tri-Oriented Mamba for 3D Medical Image Segmentation
School of Computer Science and Engineering, Beihang University（北航计算机科学与工程学院）；State Key Laboratory of Virtual Reality Technology and Systems, Beihang University（北航虚拟现实技术与系统国家重点实验室）
UltraLBM-UNet: Ultralight Bidirectional Mamba-based Model for Skin Lesion Segmentation
Data Science Institute, Vanderbilt University（维斯尼尔大学数据科学学院）；College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）；School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）
GALAR-TemporalNet v2: Anatomy-Guided Dual-Branch Temporal Classification with Bidirectional Mamba and Dual-Graph GCN for Video Capsule Endoscopy -- after competition results
School of Computer Science and Engineering, Kyungpook National University（韩国庆北国立大学计算机科学与工程学院）
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）
HARMamba: Efficient and Lightweight Wearable Sensor Human Activity Recognition Based on Bidirectional Mamba
School of Computer Science, University of South China（南华大学计算机学院）；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）；Department of Computer & Communication Engineering, University of Science and Technology Beijing（北京科技大学计算机与通信工程学院）；School of Computing, Ulster University（阿尔斯特大学计算学院）
PE-Mamba: Bidirectional Selective Layer Aggregation for AI-Generated Image Detection
College of Innovation & Technology, University of Michigan-Flint（密歇根大学弗林特分校创新与技术学院）；School of Electronics and Information Engineering, Korea Aerospace University（韩国航空大学电子与信息工程学院）
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
School of EIC, Huazhong University of Science & Technology（华中科技大学电子信息与通信学院）；Institute of Artificial Intelligence, Huazhong University of Science & Technology（华中科技大学人工智能研究院）；Horizon Robotics（地平线机器人）；Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）
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）
CMAMBADEPTH: Self-supervised Monocular Depth Estimation with Channel Mamba and Hybrid Attention
Harbin Engineering University（哈尔滨工程大学）；Key Laboratory of Advanced Marine Communication and Information Technology（先进海洋通信与信息技术重点实验室）；University of Toronto（多伦多大学）；Kanagawa University（神奈川大学）
PAS-Mamba: Phase-Amplitude-Spatial State Space Model for MRI Reconstruction
School of Computer Science and Engineering, Central South University（计算机科学与工程学院，中南大学）；Big Data Institute, Central South University（大数据研究院，中南大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Science（深圳先进技术研究所，中国科学院）；Department of Orthopaedics, The Second Xiangya Hospital of Central South University（中南大学湘雅医院骨科部）
CrossLLM-Mamba: Multimodal State Space Fusion of LLMs for RNA Interaction Prediction
Department of Computer Science, University of Kentucky, Lexington, KY, USA（计算机科学系，肯塔基大学，路易斯维尔，KY，美国）；Department of Mathematics, University of Kentucky, Lexington, KY, USA（数学系，肯塔基大学，路易斯维尔，KY，美国）；Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, USA（生物医学信息学研究所，肯塔基大学，路易斯维尔，KY，美国）
Temporally Ordered Region-Token Mamba with Logit-Space Diffusion for Remote Sensing Change Detection
Georgia Institute of Technology（佐治亚理工学院）；Indian Institute of Engineering Science and Technology (IIEST), Shibpur（印度工程科学与技术学院（IIEST），希布普尔）；Nanyang Technological University（南洋理工大学）；Indian Institute of Technology, Delhi（印度理工学院德里分校）；Duke University（杜克大学）
HSIDMamba: Exploring Bidirectional State-Space Models for Hyperspectral Denoising
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence（人机混合增强智能国家重点实验室）；National Engineering Research Center for Visual Information and Applications（视觉信息与应用国家工程研究中心）；Institute of Artificial Intelligence and Robotics（人工智能与机器人研究院）；school of software（软件学院）
SSUMamba: Spatial-Spectral Selective State Space Model for Hyperspectral Image Denoising
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）
该研究提出 SSUMamba，通过六种空间-光谱连续扫描顺序、双向 SSM 与 3D 卷积高效建模高光谱图像的长程及局部依赖，在降低每批内存消耗的同时提升去噪性能。
Efficient Self-Supervised Video Hashing with Selective State Spaces
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Peng Cheng Laboratory（鹏城实验室）；Meituan（美团）
Spherical Brownian Bridge Diffusion Models for Conditional Cortical Thickness Forecasting
Lab for AI in Medical Imaging, Technical University of Munich, Munich, Germany（人工智能医学影像实验室，慕尼黑技术大学，慕尼黑，德国）；Munich Center for Machine Learning, Munich, Germany（慕尼黑机器学习中心，慕尼黑，德国）
