Fusing LiDAR and image features in a homogeneous BEV domain has become popular for 3D object detection in autonomous driving. However, this paradigm is constrained by the excessive feature compression. While some works explore dense voxel fusion to enable better feature interaction, they face high computational costs and challenges in query generation. Additionally, feature misalignment in both domains results in suboptimal detection accuracy. To address these limitations, we propose a Dual-Domain Homogeneous Fusion network (DDHFusion), which leverages the complementarily of both BEV and voxel domains while mitigating their drawbacks. Specifically, we first transform image features into BEV and sparse voxel representations using lift-splat-shot and our proposed Semantic-Aware Feature Sampling (SAFS) module. The latter significantly reduces computational overhead by discarding unimportant voxels. Next, we introduce Homogeneous Voxel and BEV Fusion (HVF and HBF) networks for multi-modal fusion within respective domains. They are equipped with novel cross-modal Mamba blocks to resolve feature misalignment and enable comprehensive scene perception. The output voxel features are injected into the BEV space to compensate for the information loss brought by direct height compression. During query selection, the Progressive Query Generation (PQG) mechanism is implemented in the BEV domain to reduce false negatives caused by feature compression. Furthermore, we propose a Progressive Decoder (QD) that sequentially aggregates not only context-rich BEV features but also geometry-aware voxel features with deformable attention and the Multi-Modal Voxel Feature Mixing (MMVFM) block for precise classification and box regression.
MTMamba: Enhancing Multi-Task Dense Scene Understanding by Mamba-Based Decoders
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；HKUST(GZ) - SmartMore Joint Lab（香港科技大学（广州）与视博医疗联合实验室）；The Hong Kong University of Science and Technology（香港科技大学）；Southern University of Science and Technology（南方科技大学）；SmartMore（视博医疗）
ACM-UNet: Adaptive Integration of CNNs and Mamba for Efficient Medical Image Segmentation
Wuhan University of Technology（武汉理工大学）；Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院）；Zhongnan Hospital of Wuhan University（武汉大学中南医院）
CM-UNet: Hybrid CNN-Mamba UNet for Remote Sensing Image Semantic Segmentation
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息与电子工程学院）；School of Aeronautics and Astronautics, Zhejiang University（浙江大学航空航天学院）；College of Computer Science, Zhejiang University（浙江大学计算机学院）
UNetMamba: An Efficient UNet-Like Mamba for Semantic Segmentation of High-Resolution Remote Sensing Images
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；Key Laboratory of Target Cognition and Application Technology, CAS（中国科学院目标认知与应用技术重点实验室）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）
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）
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）
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）
Transformer-Progressive Mamba Network for Lightweight Image Super-Resolution
Bell Honors School, Nanjing University of Posts and Telecommunications（南京邮电大学贝尔荣誉学院）；State Key Laboratory of Integrated Services Networks, Xidian University（西安电子科技大学集成服务网络国家重点实验室）；Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院模式识别与智能系统实验室）；Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, 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智能感知与高维信息系统教育部重点实验室）；Department of Electrical Engineering and the Institute of Communications Engineering, National Tsing Hua University（国立交通大学电子工程系与通信工程研究所）
FMNet: Frequency-Assisted Mamba-Like Linear Attention Network for Camouflaged Object Detection
Shanghai University（上海大学）；Agency for Science, Technology and Research（科技研究局）；University of Electronic Science and Technology of China（电子科技大学）；National University of Singapore（新加坡国立大学）
QueryMamba: A Mamba-Based Encoder-Decoder Architecture with a Statistical Verb-Noun Interaction Module for Video Action Forecasting @ Ego4D Long-Term Action Anticipation Challenge 2024
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Fraunhofer IOSB（弗劳恩霍夫IOSB研究所）
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）
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）
OSDMamba: Enhancing Oil Spill Detection from Remote Sensing Images Using Selective State Space Model
Department of Computer Science, University of Exeter（计算机科学系，埃克塞特大学）；College of Oceanography and Space Informatics, China University of Petroleum (East China)（海洋科学与空间信息学院，中国石油大学（华东））
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 …
VM-UNet: Vision Mamba UNet for Medical Image Segmentation
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）
UINO-FSS: Unifying Representation Learning and Few-shot Segmentation via Hierarchical Distillation and Mamba-HyperCorrelation
School of Artificial Intelligence and the National Engineering Laboratory of Big Data System Computing Technology, Shenzhen University（人工智能学院和大数据系统计算技术国家工程实验室，深圳大学）；Guangdong Provincial Key Laboratory of Intelligent Information Processing, China（广东省智能信息处理重点实验室，中国）；School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University（生物医学工程学院，深圳大学医学院，深圳大学）；Department of Computer Science, University of Nottingham Ningbo China（计算机科学系，宁波大学中国）
DepMamba: Progressive Fusion Mamba for Multimodal Depression Detection
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University（复旦大学脑智能科学与技术研究院）；School of Computer Science, Fudan University（复旦大学计算机科学技术学院）
LP-DETR: Layer-wise Progressive Relations for Object Detection
New York University（纽约大学）；University of Pittsburgh（匹兹堡大学）；Fordham University（福特汉姆大学）；Georgia Institute of Technology（佐治亚理工学院）；California Institute of Technology（加州理工学院）
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）
SFD-Mamba2Net: Structure-Guided Frequency-Enhanced Dual-Stream Mamba2 Network for Coronary Artery Segmentation
Sichuan Normal University（四川师范大学）；Education Big Data Collaborative Innovation Center of Sichuan 2011（四川省教育大数据协同创新中心）；The First Affiliated Hospital with Nanjing Medical University（南京医科大学第一附属医院）；Michigan Technological University（密歇根理工大学）；Kennesaw State University（肯尼索州立大学）
MTMamba++: Enhancing Multi-Task Dense Scene Understanding via Mamba-Based Decoders
Artificial Intelligence Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）人工智能方向）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）；SmartMore Corporation Limited（SmartMore有限公司）
MambaADv2: Evolving Duality-enhanced State Space Model for Unsupervised Anomaly Detection
Zhejiang University（浙江大学）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Institute of Trustworthy Embodied AI, Fudan University（复旦大学可信具身人工智能研究所）
AVS-Mamba: Exploring Temporal and Multi-modal Mamba for Audio-Visual Segmentation
School of Information and Communication Engineering, Dalian University of Technology（大连理工大学信息与通信工程学院）；School of Innovation and Entrepreneurship, Dalian University of Technology（大连理工大学创新创业学院）；School of Artificial Intelligence, Dalian University of Technology（大连理工大学人工智能学院）
University of Science and Technology Beijing（北京科技大学）；University of Sydney（悉尼大学）；Beijing Engineering Research Center of Industrial Spectrum Imaging（北京工业光谱成像工程研究中心）；Baidu（百度）
Existing methods of multi-person video 3D human Pose and Shape Estimation (PSE) typically adopt a two-stage strategy, which first detects human instances in each frame and then performs single-person PSE with temporal model. However, the global spatio-temporal context among spatial instances can not be captured. In this paper, we propose a new end-to-end multi-person 3D Pose and Shape estimation framework with progressive Video Transformer, termed PSVT. In PSVT, a spatio-temporal encoder (STE) captures the global feature dependencies among spatial objects. Then, spatio-temporal pose decoder (S…
CMFDNet: Cross-Mamba and Feature Discovery Network for Polyp Segmentation
School of Computer Science and Technology, Changsha University of Science and Technology（长沙理工大学计算机科学与技术学院）；SCOT – Optimal Sourcing Systems Amazon.com Services LLC（SCOT——最优采购系统亚马逊公司）；School of Mechanical and Electrical Engineering, China University of Mining and Technology（中国矿业大学机械与电子工程学院）
MSV-Mamba: A Multiscale Vision Mamba Network for Echocardiography Segmentation
School of Computer and Information Engineering, Shanghai Polytechnic University（上海第二工业大学计算机与信息工程学院）；The First Affiliated Hospital of Jinan University（暨南大学附属第一医院）；Fudan University Huashan Hospital（复旦大学附属华山医院）
