Degradation-Robust Fusion: An Efficient Degradation-Aware Diffusion Framework for Multimodal Image Fusion in Arbitrary Degradation Scenarios
Department of Biomedical Engineering, Hefei University of Technology（合肥工业大学生物医学工程系）；Faculty of Information Engineering and Automation, Kunming University of Science and Technology（昆明理工大学信息工程与自动化学院）；School of Information Science and Technology, University of Science and Technology of China（中国科学技术大学信息科学技术学院）
Complex degradations like noise, blur, and low resolution are typical challenges in real world image fusion tasks, limiting the performance and practicality of existing methods. End to end neural network based approaches are generally simple to design and highly efficient in inference, but their black-box nature leads to limited interpretability. Diffusion based methods alleviate this to some extent by providing powerful generative priors and a more structured inference process. However, they are trained to learn a single domain target distribution, whereas fusion lacks natural fused data and relies on modeling complementary information from multiple sources, making diffusion hard to apply directly in practice. To address these challenges, this paper proposes an efficient degradation aware diffusion framework for image fusion under arbitrary degradation scenarios. Specifically, instead of explicitly predicting noise as in conventional diffusion models, our method performs implicit denoising by directly regressing the fused image, enabling flexible adaptation to diverse fusion tasks under complex degradations with limited steps. Moreover, we design a joint observation model correction mechanism that simultaneously imposes degradation and fusion constraints during sampling to ensure high reconstruction accuracy. Experiments on diverse fusion tasks and degradation configurations demonstrate the superiority of the proposed method under complex degradation scenarios.
The deep-learning-based image restoration and fusion methods have achieved remarkable results. However, the existing restoration and fusion methods paid little research attention to the robustness problem caused by dynamic degradation. In this paper, we propose a novel dynamic image restoration and fusion neural network, termed as DDRF-Net, which is capable of solving two problems, i.e., static restoration and fusion, dynamic degradation. In order to solve the static fusion problem of existing methods, dynamic convolution is introduced to learn dynamic restoration and fusion weights. In additi…
ControlFusion: A Controllable Image Fusion Framework with Language-Vision Degradation Prompts
Electronic Information School, Wuhan University（武汉大学电子信息学院）；School of Computer Science and Engineering, Macau University of Science and Technology（澳门科学技术大学计算机科学与工程学院）；Faculty of Computing, Harbin Institute of Technology（哈尔滨工业大学计算机学院）
IT-TextFusion: Iterative Text-Image Interaction with Text-Guided Residual Refinement for Degradation-Aware Image Fusion
School of Automation, Southeast University（东南大学自动化学院）；Chair of Robotics, Artificial Intelligence and Real-time Systems, Technical University of Munich（慕尼黑工业大学机器人、人工智能与实时系统研究所）
Physics-Inspired Degradation Models for Hyperspectral Image Fusion
Beijing Institute of Technology（北京理工大学）；Hunan University（湖南大学）；University of Science and Technology of China（中国科学技术大学）；Beijing Normal University（北京师范大学）
Multimodal image fusion aims to combine relevant information from images acquired with different sensors. In medical imaging, fused images play an essential role in both standard and automated diagnosis. In this paper, we propose a novel multimodal image fusion method based on coupled dictionary learning. The proposed method is general and can be employed for different medical imaging modalities. Unlike many current medical fusion methods, the proposed approach does not suffer from intensity attenuation nor loss of critical information. Specifically, the images to be fused are decomposed into …
A New Multimodal Medical Image Fusion based on Laplacian Autoencoder with Channel Attention
Visvesvaraya National Institute of Technology Nagpur（那格浦尔维斯瓦拉亚国家理工学院）；Universitat de Barcelona（巴塞罗那大学）；Computer Vision Center（计算机视觉中心）；All India Institute of Medical Sciences Nagpur（那格浦尔全印医学科学学院）
The success of multimodal data fusion in deep learning appears to be attributed to the use of complementary in-formation between multiple input data. Compared to their predictive performance, relatively less attention has been devoted to the robustness of multimodal fusion models. In this paper, we investigated whether the current multimodal fusion model utilizes the complementary intelligence to defend against adversarial attacks. We applied gradient based white-box attacks such as FGSM and PGD on MFNet, which is a major multispectral (RGB, Thermal) fusion deep learning model for semantic seg…
There has recently been growing interest in utilizing multimodal sensors to achieve robust lane line segmentation. In this paper, we introduce a novel multimodal fusion architecture from an information theory perspective, and demonstrate its practical utility using Light Detection and Ranging (LiDAR) camera fusion networks. In particular, we develop, for the first time, a multimodal fusion network as a joint coding model, where each single node, layer, and pipeline is represented as a channel. The forward propagation is thus equal to the information transmission in the channels. Then, we can q…
We propose a compact and effective framework to fuse multimodal features at multiple layers in a single network. The framework consists of two innovative fusion schemes. Firstly, unlike existing multimodal methods that necessitate individual encoders for different modalities, we verify that multimodal features can be learnt within a shared single network by merely maintaining modality-specific batch normalization layers in the encoder, which also enables implicit fusion via joint feature representation learning. Secondly, we propose a bidirectional multi-layer fusion scheme, where multimodal f…
Nanjing University of Posts and Telecommunications（南京邮电大学）
Due to the instability and limitations of unimodal biometric systems, multimodal systems have attracted more and more attention from researchers. However, how to exploit the independent and complementary information between different modalities remains a key and challenging problem. In this paper, we propose a multimodal biometric fusion recognition algorithm based on fingerprints and finger veins (Fingerprint Finger Veins-Channel Spatial Attention Fusion Module, FPV-CSAFM). Specifically, for each pair of fingerprint and finger vein images, we first propose a simple and effective Convolutional…
University of Toronto（多伦多大学）；The Hospital for Sick Children（病童医院）
Medical images play an important role in clinical applications. Multimodal medical images could provide rich information about patients for physicians to diagnose. The image fusion technique is able to synthesize complementary information from multimodal images into a single image. This technique will prevent radiologists switch back and forth between different images and save lots of time in the diagnostic process. In this paper, we introduce a novel Dilated Residual Attention Network for the medical image fusion task. Our network is capable to extract multi-scale deep semantic features. Furt…
IS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detection
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）；University of Macau（澳门大学）；The University of Hong Kong（香港大学）；Inceptio（嬴彻科技）；École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；Zhejiang University（浙江大学）
University of Campinas (UNICAMP)（坎皮纳斯州立大学）；Université de Lorraine（洛林大学）；Norwegian University of Science and Technology (NTNU)（挪威科技大学）
This paper proposes a learning model, based on rank-fusion graphs, for general applicability in multimodal prediction tasks, such as multimodal regression and image classification. Rank-fusion graphs encode information from multiple descriptors and retrieval models, thus being able to capture underlying relationships between modalities, samples, and the collection itself. The solution is based on the encoding of multiple ranks for a query (or test sample), defined according to different criteria, into a graph. Later, we project the generated graph into an induced vector space, creating fusion …
NMAM Institute of Technology（NMAM 理工学院）；MSR Institute of Technology（MSR 理工学院）
Multimodal medical image fusion helps to increase efficiency in medical diagnosis. This paper presents multimodal medical image fusion by selecting relevant features using Principle Component Analysis (PCA) and Particle Swarm Optimization techniques (PSO). DTCWT is used for decomposition of the images into low and high frequency coefficients. Fusion rules such as combination of minimum, maximum and simple averaging are applied to approximate and detailed coefficients. The fused image is reconstructed by inverse DTCWT. Performance metrics are evaluated and it shows that DTCWT-PCA performs bette…
U3M: Unbiased Multiscale Modal Fusion Model for Multimodal Semantic Segmentation
Institute of Artificial Intelligence (TeleAI), China Telecom（中国电信人工智能研究院（TeleAI））；School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University（西北工业大学人工智能、光学与电子学院（iOPEN））
FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（北京邮电大学网络与交换技术国家重点实验室）；School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）
In this paper, we present Fusion-GCN, an approach for multimodal action recognition using Graph Convolutional Networks (GCNs). Action recognition methods based around GCNs recently yielded state-of-the-art performance for skeleton-based action recognition. With Fusion-GCN, we propose to integrate various sensor data modalities into a graph that is trained using a GCN model for multi-modal action recognition. Additional sensor measurements are incorporated into the graph representation, either on a channel dimension (introducing additional node attributes) or spatial dimension (introducing new …
MANGO: Multimodal Attention-based Normalizing Flow Approach to Fusion Learning
CVIU Lab, University of Arkansas, USA（大学实验室，亚利桑那大学，美国）；University of Florida, USA（佛罗里达大学，美国）；COSMOS Research Center, University of Arkansas, Little Rock, USA（研究机构，亚利桑那大学，小石城，美国）；ICSI, University of California, Berkeley, USA（研究机构，加州大学伯克利分校，美国）
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。
We present MMFT-BERT(MultiModal Fusion Transformer with BERT encodings), to solve Visual Question Answering (VQA) ensuring individual and combined processing of multiple input modalities. Our approach benefits from processing multimodal data (video and text) adopting the BERT encodings individually and using a novel transformer-based fusion method to fuse them together. Our method decomposes the different sources of modalities, into different BERT instances with similar architectures, but variable weights. This achieves SOTA results on the TVQA dataset. Additionally, we provide TVQA-Visual, an…
The fusion of multimodal sensor streams, such as camera, lidar, and radar measurements, plays a critical role in object detection for autonomous vehicles, which base their decision making on these inputs. While existing methods exploit redundant information in good environmental conditions, they fail in adverse weather where the sensory streams can be asymmetrically distorted. These rare "edge-case" scenarios are not represented in available datasets, and existing fusion architectures are not designed to handle them. To address this challenge we present a novel multimodal dataset acquired in o…
Multimodal Fusion Learning with Dual Attention for Medical Imaging
Indian Institute of Technology Ropar（印度理工学院罗帕尔分校）；Deakin University（迪肯大学）；Queensland University of Technology（昆士兰科技大学）；NIMS University（NIMS大学）；Jio Institute（吉奥学院）；RMIT University（皇家墨尔本理工大学）
本文提出含多分支融合与多模态信息融合双注意力模块的DRIFA机制，构建可嵌入任意深度网络的DRIFA-Net，并用集成Monte Carlo dropout估计不确定性，在五个多模态医学数据集上取得优于现有方法的表现。
Beihang University（北京航空航天大学）；Baidu Research（百度研究院）；Beijing Institute of Technology（北京理工大学）
Accurate detection of obstacles in 3D is an essential task for autonomous driving and intelligent transportation. In this work, we propose a general multimodal fusion framework FusionPainting to fuse the 2D RGB image and 3D point clouds at a semantic level for boosting the 3D object detection task. Especially, the FusionPainting framework consists of three main modules: a multi-modal semantic segmentation module, an adaptive attention-based semantic fusion module, and a 3D object detector. First, semantic information is obtained for 2D images and 3D Lidar point clouds based on 2D and 3D segmen…
A review of deep learning-based information fusion techniques for multimodal medical image classification
Inserm（法国国家健康与医学研究院）；University of Western Brittany（西布列塔尼大学）；IMT Atlantique（大西洋电信学院）；Sorbonne University（索邦大学）；Lariboisière Hospital（拉里布瓦西耶尔医院）；AP-HP（巴黎公共医疗集团）；Paris Cité University（巴黎城市大学）；CHRU Brest（布雷斯特大学中心医院）；LaTIM UMR 1101（LaTIM联合研究单位1101）
University of Waterloo（滑铁卢大学）；Shenzhen University（深圳大学）
Multimodal learning mimics the reasoning process of the human multi-sensory system, which is used to perceive the surrounding world. While making a prediction, the human brain tends to relate crucial cues from multiple sources of information. In this work, we propose a novel multimodal fusion module that learns to emphasize more contributive features across all modalities. Specifically, the proposed Multimodal Split Attention Fusion (MSAF) module splits each modality into channel-wise equal feature blocks and creates a joint representation that is used to generate soft attention for each chann…
Tasks that rely on multi-modal information typically include a fusion module that combines information from different modalities. In this work, we develop a Refiner Fusion Network (ReFNet) that enables fusion modules to combine strong unimodal representation with strong multimodal representations. ReFNet combines the fusion network with a decoding/defusing module, which imposes a modality-centric responsibility condition. This approach addresses a big gap in existing multimodal fusion frameworks by ensuring that both unimodal and fused representations are strongly encoded in the latent fusion …
Instituto Politécnico Nacional（国立理工学院）；National University of Singapore（新加坡国立大学）；Nanyang Technological University（南洋理工大学）
Multimodal sentiment analysis is a very actively growing field of research. A promising area of opportunity in this field is to improve the multimodal fusion mechanism. We present a novel feature fusion strategy that proceeds in a hierarchical fashion, first fusing the modalities two in two and only then fusing all three modalities. On multimodal sentiment analysis of individual utterances, our strategy outperforms conventional concatenation of features by 1%, which amounts to 5% reduction in error rate. On utterance-level multimodal sentiment analysis of multi-utterance video clips, for which…
