Information Fusion最新文献

{"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}

{"title":"Tokenized EEG signals with large language models for epilepsy detection via multimodal information fusion","authors":"XingchiChen ,&nbsp;Fushen Xie ,&nbsp;Fa Zhu ,&nbsp;Shuanglong Zhang ,&nbsp;Xiaoyang Lu ,&nbsp;Qing Li ,&nbsp;Rong Chen ,&nbsp;Dazhou Li ,&nbsp;David Camacho","doi":"10.1016/j.inffus.2026.104128","DOIUrl":"10.1016/j.inffus.2026.104128","url":null,"abstract":"<div><div>The detection of epileptic seizures using multi-sensor EEG signals is a challenging task due to the inherent complexity of the signals, the variability in sensor configurations, and the difficulty in distinguishing the weak inter-class difference. To address these challenges, we propose a novel multimodal information fusion framework that integrates a large language model (LLM) and a multimodal EEG feature tokenization method for enhanced epilepsy detection. This paper adopts a multimodal feature extraction (MFE) method to effectively generate multimodal feature representations from EEG signals and extract different feature representations of EEG signals from different signal domains. In addition, we design a multimodal EEG feature tokenization method to tokenize EEG signal features and fuse the semantic information, solving the problem of fusing epileptic EEG features with semantic information in prompt words. We use the powerful reasoning and pattern recognition capabilities of pre-trained LLMs to accurately and robustly detect epileptic events. The proposed method is evaluated on a public dataset. Extensive experimental results show that the proposed method outperforms the current comparative methods in multiple performance indicators.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104128"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146006527","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":"StegaFusion: Steganography for information hiding and fusion in multimodality","authors":"Zihao Xu ,&nbsp;Dawei Xu ,&nbsp;Zihan Li ,&nbsp;Juan Hu ,&nbsp;Baokun Zheng ,&nbsp;Chuan Zhang ,&nbsp;Liehuang Zhu","doi":"10.1016/j.inffus.2026.104150","DOIUrl":"10.1016/j.inffus.2026.104150","url":null,"abstract":"<div><div>Current generative steganography techniques have attracted considerable attention due to their security. However, different platforms and social environments exhibit varying preferred modalities, and existing generative steganography techniques are often restricted to a single modality. Inspired by advancements in inpainting techniques, we observe that the inpainting process is inherently generative. Moreover, cross-modal inpainting minimally perturbs unchanged regions and shares a consistent masking-and-fill procedure. Based on these insights, we introduce StegaFusion, a novel framework for unifying multimodal generative steganography. StegaFusion leverages shared generation seeds and conditional information, which enables the receiver to deterministically reconstruct the reference content. The receiver then performs differential analysis on the inpainting-generated stego content to extract the secret message. Compared to traditional unimodal methods, StegaFusion enhances controllability, security, compatibility, and interpretability without requiring additional model training. To the best of our knowledge, StegaFusion is the first framework to formalize and unify cross-modal generative steganography, offering wide applicability. Extensive qualitative and quantitative experiments demonstrate the superior performance of StegaFusion in terms of controllability, security, and cross-modal compatibility.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104150"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033292","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":"EDGCN: An embedding-driven fusion framework for heterogeneity-aware motor imagery decoding","authors":"Chaowen Shen,&nbsp;Yanwen Zhang,&nbsp;Zejing Zhao,&nbsp;Akio Namiki","doi":"10.1016/j.inffus.2026.104170","DOIUrl":"10.1016/j.inffus.2026.104170","url":null,"abstract":"<div><div>Motor imagery electroencephalography (MI-EEG) captures neural activity associated with imagined motor tasks and has been widely applied in both basic neuroscience and clinical research. However, the intrinsic spatio-temporal heterogeneity of MI-EEG signals and pronounced inter-subject variability present major challenges for accurate decoding. Most existing deep learning methods rely on fixed architectures and shared parameters, which limits their ability to capture the complex, dynamic patterns driven by individual differences. To address these limitations, we propose an Embedding-Driven Graph Convolutional Network (EDGCN), which leverages a heterogeneity-aware spatio-temporal embedding fusion mechanism to adaptively generate graph convolutional kernel parameters from a shared embedding-driven parameter bank. Specifically, we design a Multi-Resolution Temporal Embedding (MRTE) strategy based on multi-resolution power spectral features and a Structure-Aware Spatial Embedding (SASE) mechanism that integrates both local and global connectivity structures. On this basis, we construct a heterogeneity-aware parameter generation mechanism based on Chebyshev graph convolution to effectively capture the spatiotemporal heterogeneity of EEG signals, with an orthogonality-constrained parameter space that enhances diversity and representational fusion. Experimental results demonstrate that the proposed model achieves superior classification accuracies of 86.50% and 90.14% on the BCIC-IV-2a and BCIC-IV-2b datasets, respectively, outperforming current state-of-the-art methods.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"131 ","pages":"Article 104170"},"PeriodicalIF":15.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033916","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}