Information Fusion最新文献

{"title":"Sharable and discriminative multi-view geometry-adaptive fusion network for 3D dental model segmentation","authors":"Yue Zhao ,&nbsp;Xinning Chen ,&nbsp;Kehan Li ,&nbsp;Yifan Lin ,&nbsp;Yang Liu ,&nbsp;Fan Wang","doi":"10.1016/j.inffus.2026.104196","DOIUrl":"10.1016/j.inffus.2026.104196","url":null,"abstract":"<div><div>Accurate 3D dental model segmentation is critical for digital dental treatment, as it provides valuable clinical references. <em>Existing methods</em> fail to adaptively evaluate the importance or contribution of different geometric attributes during heterogeneous features fusion, hindering the accuracy of end-to-end segmentation. <em>In this paper</em>, we pioneer the description of geometric attributes of 3D dental model as views. A multi-view geometry-adaptive fusion network (MGAFNet) is proposed to dynamically seek the optimal combination of views through distinctive and sharable features exploration for fine-grained 3D dental model segmentation. <em>Specifically</em>, during distinctive features extraction, we design geometry-aware enhancement module (GAE) to improve topological variations learning in teeth. After that, a multivariate sharable cross-interaction module (SCIM) is developed to facilitate the flow of information and capture sharable features among views. Subsequently, a multivariate adaptive representation fusion module (MARF) is implemented to adaptively balance the importance or contribution of views by constructing weight matrices for distinctive and sharable features from different feature sources. Compared to eight advanced methods, our MGAFNet achieves state-of-the-art performance on both a public benchmark and a private clinical dataset. It demonstrates robustness in handling various dental conditions (e.g., misaligned, missing and supernumerary teeth), avoiding category confusion and blurry boundary segmentation.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"132 ","pages":"Article 104196"},"PeriodicalIF":15.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vertical Federated Fuzzy Clustering via Orthogonal Semi-Nonnegative Tensor Factorization","authors":"Shunxin Cheng, Kai Cui, Yuxuan Liu, Yu Duan, Quanxue Gao","doi":"10.1016/j.inffus.2026.104598","DOIUrl":"https://doi.org/10.1016/j.inffus.2026.104598","url":null,"abstract":"Federated clustering is a privacy-preserving distributed learning paradigm that enables multiple clients to collaboratively obtain high-quality clustering results without sharing raw data. However, existing matrix factorization-based methods typically rely only on simple decision-level fusion, making it difficult to capture complementary information and latent relationships across clients. To address this issue, we propose a novel Orthogonal Semi-Nonnegative Tensor Factorization-based Vertical Federated Fuzzy Clustering method (OSNTF-VFFC). On the client side, only locally learned anchor graphs are uploaded, reducing communication overhead while preserving data privacy. On the server side, anchor graphs from all clients are stacked along the client mode to construct a third-order tensor, which is then jointly factorized via orthogonal semi-nonnegative tensor factorization. This enables collaborative fusion at both the data level and decision level, effectively capturing high-order cross-client correlations. Furthermore, a tensor Schatten <ce:italic>p</ce:italic>-norm regularization is introduced to impose low-rank constraints across clients, explicitly modeling shared structures and complementary information. Extensive experiments on multiple benchmark datasets demonstrate that the proposed method consistently outperforms state-of-the-art federated clustering approaches, validating its effectiveness.","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"14 1","pages":""},"PeriodicalIF":18.6,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148402434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"All-weather multi-modality image fusion: Unified framework and 100k benchmark","authors":"Xilai Li ,&nbsp;Wuyang Liu ,&nbsp;Xiaosong Li ,&nbsp;Fuqiang Zhou ,&nbsp;Huafeng Li ,&nbsp;Feiping Nie","doi":"10.1016/j.inffus.2026.104130","DOIUrl":"10.1016/j.inffus.2026.104130","url":null,"abstract":"<div><div>Multi-modality image fusion (MMIF) combines complementary information from different image modalities to provide a comprehensive and objective interpretation of scenes. However, existing fusion methods cannot resist diverse weather interference in real-world scenes, limiting their practical applicability. To bridge this gap, we propose an end-to-end, unified all-weather MMIF model. Rather than focusing solely on pixel-level recovery, our method emphasizes maximizing the representation of key scene information through joint feature fusion and restoration. Specifically, we first decompose images into low-rank and sparse components, enabling effective feature separation for enhanced multi-modality perception. During feature recovery, we introduce a physically-aware clear feature prediction module, inferring variations in light transmission via illumination and reflectance. Clear features generated by the network are used to enhance the representation of salient information. We also construct a large-scale MMIF dataset with 100,000 image pairs comprehensively across rain, haze, and snow conditions, as well as covering various degradation levels and diverse scenes. Experimental results in both real-world and synthetic scenes demonstrate that the proposed method excels in image fusion and downstream tasks such as object detection, semantic segmentation, and depth estimation. The source code is available at <span><span>https://github.com/ixilai/AWFusion</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104130"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ELOGOnet: Knowledge-enhanced local-global learning for cardiac diagnosis","authors":"Yizhuo Feng ,&nbsp;Beibei Wang ,&nbsp;Zirui Wang ,&nbsp;Ke Jiang ,&nbsp;Peng Wang ,&nbsp;Lidong Du ,&nbsp;Xianxiang Chen ,&nbsp;Pang Wu ,&nbsp;Zhenfeng Li ,&nbsp;Junxian Song ,&nbsp;Libin Jiang ,&nbsp;Zhen Fang","doi":"10.1016/j.inffus.2026.104168","DOIUrl":"10.1016/j.inffus.2026.104168","url":null,"abstract":"<div><div>The diagnostic process of a human cardiologist is a holistic act of reasoning that seamlessly integrates two key components: (1) a synergistic analysis of the ECG signal itself, combining insights from both global rhythmic patterns and local morphologies; and (2) a prior-informed interpretation process that leverages internalized medical priors and external patient-specific information. However, existing deep learning models struggle to emulate this complex expert reasoning, often facing a dual dilemma: a failure to synergize local and global features within a unified framework, and a widespread neglect of valuable, low-cost prior knowledge sources like disease associations and patient metadata. To bridge this gap, we propose ELOGOnet, a novel deep learning framework designed to model the expert diagnostic workflow. Modeling the expert’s synergistic signal analysis, ELOGOnet employs a parallel hybrid architecture that integrates a State Space Model (SSM) for global rhythms and a CNN for local morphologies. Enabling a prior-informed interpretation, the framework incorporates two key innovations: an association loss that enhances clinical coherence by modeling disease comorbidity and mutual exclusivity, and an adaptive cross-gating module for the robust fusion of patient metadata. Extensive experiments on several mainstream public benchmarks demonstrate that ELOGOnet establishes a new state-of-the-art by achieving an average Macro-F1 of 63.8% across 8 multi-label tasks and consistently outperforming 16 competitive baselines, thereby setting a new performance benchmark for automated cardiac diagnosis from ECG.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104168"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GeoCraft: A Diffusion Model-based 3D Reconstruction Method driven by image and point cloud fusion","authors":"Weixuan Ma ,&nbsp;Yamin Li ,&nbsp;Chujin Liu ,&nbsp;Hao Zhang ,&nbsp;Jie Li ,&nbsp;Kansong Chen ,&nbsp;Weixuan Gao","doi":"10.1016/j.inffus.2026.104149","DOIUrl":"10.1016/j.inffus.2026.104149","url":null,"abstract":"<div><div>With the rapid development of technologies like virtual reality (VR), autonomous driving, and digital twins, the demand for high-precision and realistic multimodal 3D reconstruction has surged. This technology has become a core research focus in computer vision and graphics due to its ability to integrate multi-source data, such as 2D images and point clouds. However, existing methods face challenges such as geometric inconsistency in single-view reconstruction, poor point cloud-to-mesh conversion, and insufficient multimodal feature fusion, limiting their practical application. To address these issues, this paper proposes GeoCraft, a multimodal 3D reconstruction method that generates high-precision 3D models from 2D images through three collaborative stages: Diff2DPoint, Point2DMesh, and Vision3DGen. Specifically, Diff2DPoint generates an initial point cloud with geometric alignment using a diffusion model and projection feature fusion; Point2DMesh converts the point cloud into a high-quality mesh using an autoregressive decoder-only Transformer and Direct Preference Optimization (DPO); Vision3DGen creates high-fidelity 3D objects through multimodal feature alignment. Experiments on the Google Scanned Objects (GSO) and Pix3D datasets show that GeoCraft excels in key metrics. On the GSO dataset, its CMMD is 2.810 and FID_CLIP is 26.420; on Pix3D, CMMD is 3.020 and FID_CLIP is 27.030. GeoCraft significantly outperforms existing 3D reconstruction methods and also demonstrates advantages in computational efficiency, effectively solving key challenges in 3D reconstruction.The code is available at <span><span>https://github.com/weixuanma/GeoCraft</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104149"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PGSC: A gradient sparsification communication optimization criterion for nonequilibrium thermodynamics","authors":"Wenlong Zhang ,&nbsp;Ying Li ,&nbsp;Hanhan Du ,&nbsp;Yan Wei ,&nbsp;Aiqing Fang","doi":"10.1016/j.inffus.2026.104188","DOIUrl":"10.1016/j.inffus.2026.104188","url":null,"abstract":"<div><div>Gradient compression can reduce communication overhead. However, current static sparsity techniques may disturb gradient dynamics, resulting in unstable model convergence and reduced feature discriminative ability, whereas transmitting the complete gradient leads to high costs. To address this issue, inspired by nonequilibrium thermodynamics, this paper proposes a Physics-guided Gradient Sparsification Criterion (PGSC). Specifically, we formulate a continuous field equation based on the gradient magnitude distribution, deriving an adaptive decay rule for the sparsification threshold during the training phase. We then dynamically adjust the sparsification threshold according to this rule, effectively addressing the complexity of multimodal features and ensuring consistent information transmission. Our method achieves adaptive co-optimization of gradient compression and model accuracy by establishing a dynamic equilibrium mechanism between gradient dissipation and information entropy. This approach ensures stable convergence rates while preserving the gradient structure of multi-scale features. Extensive experiments on public datasets, including CIFAR-10, MNIST, and FLIR_ADAS_v2, demonstrate significant advantages over competitors such as TopK and quantization compression, while also reducing communication costs.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104188"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146047982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A survey of multimodal fusion for Alzheimer’s disease prediction: A new taxonomy and trends","authors":"Yifan Guan ,&nbsp;Wei Wang ,&nbsp;Jianjun Chen ,&nbsp;Po Yang ,&nbsp;Jingzhou Xu ,&nbsp;Jun Qi","doi":"10.1016/j.inffus.2025.104098","DOIUrl":"10.1016/j.inffus.2025.104098","url":null,"abstract":"<div><div>Alzheimer’s disease (AD) is a neurodegenerative disease, well-known for its incurability, and is common among the elderly population worldwide. Previous studies have demonstrated that early intervention positively influences disease progression, leading to increased research into pathological analysis and disease trajectory prediction through machine learning (ML) methods. Given the similarities across different neurodegenerative disorders, a diagnosis relying solely upon a single modality of data is inadequate. Consequently, current research predominantly focuses on multimodal analysis, integrating medical imaging and clinical patient information, with continuous identification of new data types potentially aiding AD diagnosis. Multimodal approaches have been explored extensively over the past two decades, with significant advances observed following the introduction of Deep Learning (DL) techniques. Deep neural networks can adaptively extract and fuse features directly from input data, significantly broadening the scope of multimodal analysis. However, earlier classification studies have primarily concentrated on traditional ML, often neglecting the rapid advancements in DL networks. This article provides a comprehensive description of the acquisition pathways based on modalities, discusses the modalities currently used for research in neuroimaging, human body fluids, and other relevant sources. Additionally, it classifies fusion methodologies utilised in both DL and traditional ML contexts, highlights existing challenges, and outlines potential directions for future research.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104098"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145845503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GIAFormer: A Gradient-Infused Attention and Transformer for Pain Assessment with EDA-fNIRS Fusion","authors":"Muhammad Umar Khan ,&nbsp;Girija Chetty ,&nbsp;Stefanos Gkikas ,&nbsp;Manolis Tsiknakis ,&nbsp;Roland Goecke ,&nbsp;Raul Fernandez-Rojas","doi":"10.1016/j.inffus.2026.104173","DOIUrl":"10.1016/j.inffus.2026.104173","url":null,"abstract":"<div><div>Reliable pain assessment is crucial in clinical practice, yet it remains a challenge because self-report-based assessment is inherently subjective. In this work, we introduce GIAFormer, a deep learning framework designed to provide an objective measure of multilevel pain by jointly analysing Electrodermal Activity (EDA) and functional Near-Infrared Spectroscopy (fNIRS) signals. By combining the complementary information from autonomic and cortical responses, the proposed model aims to capture both physiological and neural aspects of pain. GIAFormer integrates a Gradient-Infused Attention (GIA) module with a Transformer. The GIA module enhances signal representation by fusing the physiological signals with their temporal gradients and applying spatial attention to highlight inter-channel dependencies. The Transformer component follows, enabling the model to learn long-range temporal relationships. The framework was evaluated on the AI4Pain dataset comprising 65 subjects using a leave-one-subject-out validation protocol. GIAFormer achieved an accuracy of 90.51% and outperformed recent state-of-the-art approaches. These findings highlight the potential of gradient-aware attention and multimodal fusion for interpretable, non-invasive, and generalisable pain assessment suitable for clinical and real-world applications.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104173"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MulMoSenT: Multimodal sentiment analysis for a low-resource language using textual-visual cross-attention and fusion","authors":"Sadia Afroze ,&nbsp;Md. Rajib Hossain ,&nbsp;Mohammed Moshiul Hoque ,&nbsp;Nazmul Siddique","doi":"10.1016/j.inffus.2026.104129","DOIUrl":"10.1016/j.inffus.2026.104129","url":null,"abstract":"<div><div>The widespread availability of the Internet and the growing use of smart devices have fueled the rapid expansion of multimodal (image-text) sentiment analysis (MSA), a burgeoning research field. This growth is driven by the massive volume of image-text data generated by these technologies. However, MSA faces significant challenges, notably the misalignment between images and text, where an image may carry multiple interpretations or contradict its paired text. In addition, short textual content often lacks sufficient context, complicating sentiment prediction. These issues are particularly acute in low-resource languages, where annotated image-text corpora are scarce, and Vision-Language Models (VLMs) and Large Language Models (LLMs) exhibit limited performance. This research introduces <strong>MulMoSenT</strong>, a multimodal image-text sentiment analysis system tailored to tackle these challenges for low-resource languages. The development of <strong>MulMoSenT</strong> unfolds across four key phases: corpus development, baseline model evaluation and selection, hyperparameter adaptation, and model fine-tuning and inference. The proposed <strong>MulMoSenT</strong> model achieves a peak accuracy of 84.90%, surpassing all baseline models. Delivers a 37. 83% improvement over VLMs, a 35.28% gain over image-only models, and a 0.71% enhancement over text-only models. Both the dataset and the solution are publicly accessible at: <span><span>https://github.com/sadia-afroze/MulMoSenT</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104129"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ExInCOACH: Strategic exploration meets interactive tutoring for context-aware game onboarding","authors":"Rui Hua ,&nbsp;Zhaoyu Huang ,&nbsp;Jinhao Lu ,&nbsp;Yakun Li ,&nbsp;Na Zhao","doi":"10.1016/j.inffus.2026.104151","DOIUrl":"10.1016/j.inffus.2026.104151","url":null,"abstract":"<div><div>Traditional game tutorials often fail to deliver real-time contextual guidance, providing static instructions disconnected from dynamic gameplay states. This limitation stems from their inability to interpret evolving game environments and generate high-quality decisions during live player interactions. We present ExInCOACH, a hybrid framework that synergizes exploratory reinforcement learning (RL) with interactive large language models (LLMs) to enable state-aware adaptive tutoring. Our framework first employs deep RL to discover strategic patterns via self-play, constructing a Q-function. During player onboarding, LLMs map the Q-values of currently legal actions and their usage conditions into natural language rule explanations and strategic advice by analyzing live game states and player decisions.</div><div>Evaluations in Dou Di Zhu (a turn-based card game) reveal that learners using ExInCOACH experienced intuitive strategy internalization-all participants reported grasping advanced tactics faster than through rule-based tutorials, while most players highly valued the real-time contextual feedback. A comparative study demonstrated that players trained with ExInCOACH achieved a 70% win rate (14 wins/20 games) against those onboarded via traditional methods, as they benefited from adaptive guidance that evolved with their skill progression. To further validate the framework’s generalizability, evaluations were also conducted in StarCraft II, a high-complexity real-time strategy (RTS) game. In 2v2 cooperative battles, teams trained with ExInCOACH achieved a 66.7% win rate against teams assisted by Vision LLMs (VLLMs) and an impressive 100% win rate against teams relying on traditional static game wikis for learning. Cognitive load assessments indicated that ExInCOACH significantly reduced players- mental burden and frustration in complex scenarios involving real-time decision-making and multi-unit collaboration, while also outperforming traditional methods in information absorption efficiency and tactical adaptability. This work proposes a game tutorial design paradigm based on RL model exploration &amp; LLM rule interpretation, making AI-generated strategies accessible through natural language interaction tailored to individual learning contexts.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104151"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}