Cross-modality fusing complementary information from different modalities effectively improves object detection performance, making it more useful and robust for a wider range of applications. Existing fusion strategies combine different types of images or merge different backbone features through elaborated neural network modules. However, these methods neglect that modality disparities affect cross-modality fusion performance, as different modalities with different camera focal lengths, placements, and angles are hardly fused. In this paper, we investigate cross-modality fusion by associating cross-modal features in a hidden state space based on an improved Mamba with a gating mechanism. We design a Fusion-Mamba block (FMB) to map cross-modal features into a hidden state space for interaction, thereby reducing disparities between cross-modal features and enhancing the representation consistency of fused features. FMB contains two modules: the State Space Channel Swapping (SSCS) module facilitates shallow feature fusion, and the Dual State Space Fusion (DSSF) enables deep fusion in a hidden state space. Through extensive experiments on public datasets, our proposed approach outperforms the state-of-the-art methods on $m$AP with 5.9% on $M^3FD$ and 4.9% on FLIR-Aligned datasets, demonstrating superior object detection performance. To the best of our knowledge, this is the first work to explore the potential of Mamba for cross-modal fusion and establish a new baseline for cross-modality object detection.
DEPFusion: Dual-Domain Enhancement and Priority-Guided Mamba Fusion for UAV Multispectral Object Detection
School of Information Engineering, Guangdong University of Technology（广东工业大学信息工程学院）；School of Electronic and Information Engineering, South China University of Technology（华南理工大学电子信息学院）；School of Artificial Intelligence, XiDian University（西安电子科技大学人工智能学院）
COMO: Cross-Mamba Interaction and Offset-Guided Fusion for Multimodal Object Detection
School of Resource and Environmental Sciences, Wuhan University（武汉大学资源与环境科学学院）；State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）；School of Mathematics and Statistics, University of Glasgow（格拉斯哥大学数学与统计学院）；School of Geophysics and Geomatics, China University of Geoscience（中国地质大学（武汉）地球物理与空间信息学院）
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）
MM-DETR: An Efficient Multimodal Detection Transformer with Mamba-Driven Dual-Granularity Fusion and Frequency-Aware Modality Adapters
School of Information and Electronics, Beijing Institute of Technology（信息与电子学院，北京理工大学）；Beijing Institute of Technology Chongqing Innovation Center（北京理工大学重庆创新中心）；National Key Laboratory for Space-Born Intelligent Information Processing（空间智能信息处理国家级重点实验室）；School of Automation, Beijing Institute of Technology（自动化学院，北京理工大学）
MambaTrans: Multimodal Fusion Image Translation via Large Language Model Priors for Downstream Visual Tasks
School of Physics and Optoelectronic Engineering（物理与光电工程学院）；Guangdong-HongKong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology（粤港澳联合智能微纳光电技术实验室）；School of Information Engineering and Automation（信息工程与自动化学院）
本文提出MambaTrans，一种多模态融合图像模态翻译器，利用多模态大语言模型描述和语义分割掩码作为输入，通过Multi-Model State Space Block捕获跨模态依赖，在不调整预训练模型参数的情况下提升下游视觉任务性能。
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）
CFMW: Cross-modality Fusion Mamba for Robust Object Detection under Adverse Weather
School of Computer Science and Technology, Zhejiang University of Technology（浙江工业大学计算机科学与技术学院）；USC Viterbi School of Engineering, the University of Southern California（南加州大学维特比工程学院）；College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）；School of Robotics, Hunan University（湖南大学机器人学院）
RemoteDet-Mamba: A Hybrid Mamba-CNN Network for Multi-modal Object Detection in Remote Sensing Images
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China（计算机科学学院，北京邮电大学）；School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China（电子工程学院，北京邮电大学）
MambaFusion: Height-Fidelity Dense Global Fusion for Multi-modal 3D Object Detection
State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), CASIA（多模态人工智能系统国家重点实验室（MAIS），中国科学院自动化所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能学院）；Anyverse Intelligence；Beijing Key Laboratory of Super Intelligent Security of Multi-Modal Information（北京多模态信息超级智能安全重点实验室）；School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）
Axial-Relation Guided Fusion State Space Model for Optical-Elevation Sensing Image Segmentation
State Key Laboratory of Physical Oceanography, Ocean University of China（中国海洋大学物理海洋学国家重点实验室）；Department of Electrical and Computer Engineering, Mississippi State University（密苏里州立大学电气与计算机工程系）
School of Aerospace Engineering, Tsinghua University（清华大学航天航空学院）
Cross-modality fusing complementary information of multispectral remote sensing image pairs can improve the perception ability of detection algorithms, making them more robust and reliable for a wider range of applications, such as nighttime detection. Compared with prior methods, we think different features should be processed specifically, the modality-specific features should be retained and enhanced, while the modality-shared features should be cherry-picked from the RGB and thermal IR modalities. Following this idea, a novel and lightweight multispectral feature fusion approach with joint…
DLRMamba: Distilling Low-Rank Mamba for Edge Multispectral Fusion Object Detection
National Space Science Center, Chinese Academy of Sciences（中国科学院国家空间科学中心）；School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术系）；School of Electronic Engineering and Computer Science, Queen Mary University of London（伦敦大学Queen Mary电子工程与计算机科学学院）；School of Astronomy and Space Science, University of Chinese Academy of Sciences（中国科学院大学天文与空间科学系）
DMM: Disparity-guided Multispectral Mamba for Oriented Object Detection in Remote Sensing
University of Electronic Science and Technology of China（电子科技大学）；Beijing Institute of Space Mechanics and Electricity（北京空间机电研究所）；Donghai Laboratory（东海实验室）
Multispectral image pairs can provide the combined information, making object detection applications more reliable and robust in the open world. To fully exploit the different modalities, we present a simple yet effective cross-modality feature fusion approach, named Cross-Modality Fusion Transformer (CFT) in this paper. Unlike prior CNNs-based works, guided by the transformer scheme, our network learns long-range dependencies and integrates global contextual information in the feature extraction stage. More importantly, by leveraging the self attention of the transformer, the network can natu…
Scene depth information can help visual information for more accurate semantic segmentation. However, how to effectively integrate multi-modality information into representative features is still an open problem. Most of the existing work uses DCNNs to implicitly fuse multi-modality information. But as the network deepens, some critical distinguishing features may be lost, which reduces the segmentation performance. This work proposes a unified and efficient feature selectionand-fusion network (FSFNet), which contains a symmetric cross-modality residual fusion module used for explicit fusion o…
Backbone-Conditional Behavior of Modality Gating in Multi-Modal Prostate MRI Segmentation: A 5-Fold Cross-Validation and Gate Mechanism Analysis
The Second Xiangya Hospital of Central South University（中南大学湘雅医学院第二医院）；The Third Xiangya Hospital of Central South University（中南大学湘雅医学院第三医院）；School of Life Sciences, Central South University（中南大学生命科学学院）；Hunan Provincial Key Laboratory of Medical Information Research (Central South University)（湖南省医学信息研究重点实验室（中南大学））；Hunan Provincial Clinical Medical Research Center for Cardiovascular Intelligent Medicine（湖南省心血管智能医学临床医学研究中心）
ME-Mamba: Multi-Expert Mamba with Efficient Knowledge Capture and Fusion for Multimodal Survival Analysis
Digital Medical Research Center, School of Basic Medical Science, Fudan University, Shanghai 200032, China（复旦大学基础医学学院数字医学研究中心）；Shanghai Key Lab of Medical Image Computing（上海医学图像计算重点实验室）
Global and Local Mamba Network for Multi-Modality Medical Image Super-Resolution
School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；Hunan Engineering Research Center of Machine Vision and Intelligent Medicine, Central South University（中南大学机器视觉与智能医学湖南省工程研究中心）；University of Rouen-Normandy（鲁昂-诺曼底大学）；Henri Becquerel Cancer Center（亨利·贝克勒尔癌症中心）
Mamba-Based Modality Disentanglement Network for Multi-Contrast MRI Reconstruction
School of Communication and Information Engineering, Shanghai University（上海大学通信与信息工程学院）；School of Computer Science and Technology, East China Normal University（东华大学计算机科学与技术学院）
An approach with Visual and Tabular Mamba to multimodal medical data using Mixed Fusion
Labcin - Nature Inspired Computing Lab, Federal University of Esp\'irito Santo, Vit\'oria, Brazil PPGI - Graduate Program in Computer Science, Federal University of Esp\'irito Santo, Vit\'oria, Brazil
MambaMorph: a Mamba-based Framework for Medical MR-CT Deformable Registration
Image Processing Center, Beihang University, Beijing, China（北京航空航天大学图像处理中心）；Robotics Institute, Beihang University, Beijing, China（北京航空航天大学机器人研究所）；Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China（华中科技大学同济医院）
