Information Fusion最新文献_第10页

FUNet: Frequency Channel Multi-Modal Fusion and Uncertain Region Adjustment Network for brain tumor segmentation FUNet：用于脑肿瘤分割的信道多模态融合及不确定区域调整网络

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-07-05 DOI: 10.1016/j.inffus.2025.103474

Yu Yan , Lei Zhang , Jiayi Li , Leyi Zhang , Zhang Yi

{"title":"FUNet: Frequency Channel Multi-Modal Fusion and Uncertain Region Adjustment Network for brain tumor segmentation","authors":"Yu Yan , Lei Zhang , Jiayi Li , Leyi Zhang , Zhang Yi","doi":"10.1016/j.inffus.2025.103474","DOIUrl":"10.1016/j.inffus.2025.103474","url":null,"abstract":"<div><div>Multi-modal images are crucial for enhancing the performance of brain tumor segmentation. Existing multi-modal brain tumor segmentation methods have the following three main shortcomings: To begin with, framework design remains underexplored in current research. Secondly, effectively fusing multi-modal data, which characterize brain tumors differently, poses a significant challenge. Finally, uncertain and error-prone regions may exist within the fused features, complicating subsequent analysis. To address these issues, we propose Frequency Channel Multi-Modal Fusion and Uncertain Region Adjustment Network (FUNet). FUNet employs a triple-parallel-stream framework to integrate multi-modal information. In the encoder of the multi-modal information learning stream, we design a frequency channel multi-modal fusion module (FCMM), which distinguishes between the complementarity and redundancy of the modal information and mines the intrinsic connection. Additionally, in the decoder, we design an uncertain region adjustment module (URAM), which generates an adjustment factor to enable pixel-wise adjust uncertain error-prone regions existing in the fused features. Experiments on BrsTS 2018 and BraTS-PED 2023 demonstrate that our method achieves better results than other state-of-the-art methods.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"125 ","pages":"Article 103474"},"PeriodicalIF":14.7,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572763","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}

引用次数: 0

C2M-DoT: Cross-modal consistent multi-view medical report generation with domain transfer network C2M-DoT：跨模式一致的多视图医疗报告生成与域传输网络

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-07-05 DOI: 10.1016/j.inffus.2025.103442

Ruizhi Wang , Zhenghua Xu , Xiangtao Wang , Weipeng Liu , Thomas Lukasiewicz

{"title":"C2M-DoT: Cross-modal consistent multi-view medical report generation with domain transfer network","authors":"Ruizhi Wang , Zhenghua Xu , Xiangtao Wang , Weipeng Liu , Thomas Lukasiewicz","doi":"10.1016/j.inffus.2025.103442","DOIUrl":"10.1016/j.inffus.2025.103442","url":null,"abstract":"<div><h3>Objectives:</h3><div>In clinical practice, multiple medical images from different views provide valuable complementary information for diagnosis. However, existing medical report generation methods struggle to fully integrate multi-view data, and their reliance on multi-view input during inference limits practical applicability. Moreover, conventional word-level optimization often neglects the semantic alignment between images and reports, leading to inconsistencies and reduced diagnostic reliability. This paper aims to address these limitations and improve the performance and efficiency of medical report generation.</div></div><div><h3>Methods:</h3><div>We propose C2M-DoT, a cross-modal consistent multi-view medical report generation method with domain transfer network. C2M-DoT (i) uses semantic-based contrastive learning to fuse multi-view information to enhance lesion representation, (ii) uses domain transfer network to bridge the gap in inference performance across views, (iii) uses cross-modal consistency loss to promote personalized alignment of multi-modalities and achieve end-to-end joint optimization.</div></div><div><h3>Novelty and Findings:</h3><div>C2M-DoT pioneered the use of multi-view contrastive learning for the high semantic level of report decoding, and used a domain transfer network to overcome the data dependency of multi-view models, while enhancing the semantic matching of images and reports through cross-modal consistency optimization. Extensive experiments show that C2M-DoT outperforms state-of-the-art baselines and achieves a BLEU-4 of 0.159 and a ROUGE-L of 0.380 on the IU X-ray dataset, and a BLEU-4 of 0.193 and a ROUGE-L of 0.385 on the MIMIC-CXR dataset.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"125 ","pages":"Article 103442"},"PeriodicalIF":14.7,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634445","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}

引用次数: 0

SPGFusion: Semantic Prior Guided Infrared and visible image fusion via pretrained vision models SPGFusion：基于预训练视觉模型的语义先验制导红外和可见光图像融合

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-07-04 DOI: 10.1016/j.inffus.2025.103433

Huiqin Zhang , Shihan Yao , Jiayi Ma , Junjun Jiang , Yanduo Zhang , Huabing Zhou

{"title":"SPGFusion: Semantic Prior Guided Infrared and visible image fusion via pretrained vision models","authors":"Huiqin Zhang , Shihan Yao , Jiayi Ma , Junjun Jiang , Yanduo Zhang , Huabing Zhou","doi":"10.1016/j.inffus.2025.103433","DOIUrl":"10.1016/j.inffus.2025.103433","url":null,"abstract":"<div><div>Image fusion integrates multiple images of the same scene into a single enhanced image, improving visual clarity and supporting high-level vision tasks. Existing infrared–visible image fusion methods, while increasing semantic detail, rely heavily on labeled data, limiting flexibility and failing to capture unique features of different objects across modalities—features critical for human perception. These limitations hinder effective adaptive fusion. To address this, we propose SPGFusion: a Semantic Prior Guided infrared and visible image Fusion method without manual annotations. SPGFusion utilizes the global semantic alignment capability of CLIP, which associates visual features with human natural-language knowledge, enabling comprehensive understanding of the global semantic structures of images. Concurrently, DINO’s ability to cluster semantically similar features captures fine-grained local semantic details. The complementary combination of global and local semantic priors enables the model to achieve a comprehensive, detailed, and label-free semantic understanding of the source images, effectively overcoming the annotation dependency issue encountered by existing methods. These priors guide the fusion process through a specially designed Semantic Adaptive Fusion Network, enabling adaptive, semantically-aware fusion that highlights modality-specific features. Finally, a visual feature decoder synthesizes the fused image, capturing critical semantic details from each source. By leveraging robust, label-free semantic priors, SPGFusion gains a deeper understanding of infrared and visible source images, allowing adaptive fusion of essential features across modalities. Extensive evaluations on public datasets demonstrate that SPGFusion outperforms current state-of-the-art methods in both visual quality and semantic accuracy. The source code is available at <span><span>https://github.com/Huiqin-Zhang/SPGFusion</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"125 ","pages":"Article 103433"},"PeriodicalIF":14.7,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634444","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}

引用次数: 0

DyFuse: Dynamic fusion for weakly-supervised semantic segmentation in autonomous driving scenes DyFuse：自动驾驶场景中弱监督语义分割的动态融合

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-07-04 DOI: 10.1016/j.inffus.2025.103471

Yongqiang Li , Chuanping Hu , Kai Ren , Hao Xi , Jinhao Fan

{"title":"DyFuse: Dynamic fusion for weakly-supervised semantic segmentation in autonomous driving scenes","authors":"Yongqiang Li , Chuanping Hu , Kai Ren , Hao Xi , Jinhao Fan","doi":"10.1016/j.inffus.2025.103471","DOIUrl":"10.1016/j.inffus.2025.103471","url":null,"abstract":"<div><div>Weakly supervised semantic segmentation (WSSS) provides a compelling solution to reduce annotation costs in autonomous driving perception systems. However, existing methods fail to meet the distinct challenges of driving scenes, such as dramatic scale variations, intricate spatial relationships, and stringent boundary precision demands. To address these, we introduce DyFuse, a novel framework that seamlessly blends CLIP’s semantic understanding with SAM’s robust visual representations for WSSS in driving scenes. Our approach introduces three core innovations: (1) a Multi-scale Context Perception Enhancement (MCPE) module that captures objects across scales via parallel processing branches; (2) a dynamic fusion mechanism that adaptively merges complementary features based on reliability estimates; and (3) an intelligent pseudo-label strategy that combines CLIP’s semantic insights with SAM’s boundary accuracy. Extensive experiments demonstrate that DyFuse outperforms previous methods, increasing mIoU by 23.3% on Cityscapes, 26.5% on CamVid, and 18.3% on WildDash2, with consistent robustness in adverse weather. Remarkably, using only image-level labels, it narrows the gap with fully supervised methods to under 3% in Cityscapes and even surpasses them on CamVid validation set. Ablation studies highlight the impact of each component, especially in enhancing the segmentation of small objects and the complex boundary delineation that is critical to driving safety.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"125 ","pages":"Article 103471"},"PeriodicalIF":14.7,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144569988","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}

引用次数: 0

FaceExpr: Personalized facial expression generation via attention-focused U-Net feature fusion in diffusion models FaceExpr：通过扩散模型中以注意力为中心的U-Net特征融合生成个性化面部表情

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-07-04 DOI: 10.1016/j.inffus.2025.103431

Muhammad Sher Afgan , Bin Liu , Mamoona Naveed Asghar , Wajahat Khalid , Kai Zou , Dianmo Sheng

{"title":"FaceExpr: Personalized facial expression generation via attention-focused U-Net feature fusion in diffusion models","authors":"Muhammad Sher Afgan , Bin Liu , Mamoona Naveed Asghar , Wajahat Khalid , Kai Zou , Dianmo Sheng","doi":"10.1016/j.inffus.2025.103431","DOIUrl":"10.1016/j.inffus.2025.103431","url":null,"abstract":"<div><div>Text-to-image diffusion models have revolutionized image generation by creating high-quality visuals from text descriptions. Despite their potential for personalized text-to-image applications, existing standalone methods have struggled to provide effective semantic modifications, while approaches relying on external embeddings are computationally complex and often compromise identity and face fidelity. To overcome these challenges, we propose <strong>FaceExpr</strong>, an innovative three-instance framework using standalone text-to-image models that provide accurate facial semantic modifications and synthesize facial images with diverse expressions, all while preserving the subject’s identity. Specifically, we introduce a person-specific fine-tuning approach with two key components: (1) Attention-Focused Fusion, which uses an attention mechanism to align identity and expression features by focusing on critical facial landmarks, preserving the subject’s identity, and (2) Expression Text Embeddings, integrated into the U-Net denoising module to resolve language ambiguities and enhance expression accuracy. Additionally, an expression crafting loss is employed to strengthen the alignment between identity and expression. Furthermore, by leveraging the prior preservation loss, we enable the synthesis of expressive faces in diverse scenes, views, and conditions. FaceExpr establishes state-of-the-art performance over both standalone and hybrid methods, demonstrating its effectiveness in controllable facial expression generation. It shows strong potential for personalized content generation in digital storytelling, immersive virtual environments, and advanced research applications. For code please visit: <span><span>https://github.com/MSAfganUSTC/FaceExpr.git</span><svg><path></path></svg></span></div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"125 ","pages":"Article 103431"},"PeriodicalIF":14.7,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579978","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}

引用次数: 0

Neural Connected Kernel based Multiple Kernel Clustering 基于神经连接核的多核聚类

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-07-04 DOI: 10.1016/j.inffus.2025.103460

Jiangyuan Wang , Qiong Liu , Mingjie Cai , Weiping Ding

引用次数: 0

DiffuShield: Flexible privacy-preserving synthetic face generation via generative diffusion model diffusshield：通过生成扩散模型生成灵活的保护隐私的合成人脸

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-07-03 DOI: 10.1016/j.inffus.2025.103451

Tao Wang , Xiaoyu Chen , Zhiquan Liu , Shihong Yao

{"title":"DiffuShield: Flexible privacy-preserving synthetic face generation via generative diffusion model","authors":"Tao Wang , Xiaoyu Chen , Zhiquan Liu , Shihong Yao","doi":"10.1016/j.inffus.2025.103451","DOIUrl":"10.1016/j.inffus.2025.103451","url":null,"abstract":"<div><div>GenAI harnesses vast amounts of personal data, specifically facial images, to achieve remarkable generative capabilities. As it continues to evolve and integrate into various applications, this practice has raised significant privacy concerns. Current state-of-the-art facial privacy-preserving methods predominantly utilize a generative paradigm, where sensitive facial features, such as identity information or specific attributes, are extracted and subsequently sanitized. The fusion of these sanitized feature with other non- or soft-biometric attributes is executed to generate privacy-ensured synthetic faces. However, these methods always generate faces of insufficient security and low realism. To address these challenges, we propose DiffuShield, a novel facial privacy-preserving method that integrates identity and attribute hiding based on diffusion model. DiffuShield enables configurable, confidential, and practical facial privacy preservation by allowing selective preservation of identities and sensitive attributes. It adopts the diffusion model as the backbone generative network and introduces identity and attribute encoders as conditional inputs. These two encoders can efficiently decompose facial representations while incorporating differential privacy mechanisms by introducing Laplacian noise, thereby achieving flexible facial privacy preservation. Our experimental results demonstrate that DiffuShield not only excels in preserving privacy but also maintains image quality and compatibility with various computer vision tasks. This balance of privacy and performance positions DiffuShield as a robust solution for real-world applications requiring both privacy preservation and functionality.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"125 ","pages":"Article 103451"},"PeriodicalIF":14.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557239","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}

引用次数: 0

MCDF: Multimodal information fusion and causal analysis for election misinformation detection 基于多模态信息融合和原因分析的选举误报检测

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-07-02 DOI: 10.1016/j.inffus.2025.103470

Lazarus Kwao , Jing Ma , Sophyani Banaamwini Yussif , Matthew Quayson

{"title":"MCDF: Multimodal information fusion and causal analysis for election misinformation detection","authors":"Lazarus Kwao , Jing Ma , Sophyani Banaamwini Yussif , Matthew Quayson","doi":"10.1016/j.inffus.2025.103470","DOIUrl":"10.1016/j.inffus.2025.103470","url":null,"abstract":"<div><div>The rapid spread of election-related misinformation on social media poses a serious threat to public trust, democratic decision-making, and social stability. This form of misinformation is particularly persuasive and difficult to detect as it uses different types of content (modalities), including text, images, captions, and social interactions. These challenges undermine efforts to ensure trustworthy elections and enable timely intervention by policymakers and fact-checkers. However, existing detection approaches struggle with feature misalignment, cross-modal inconsistencies, and noisy social data, thereby limiting their ability to accurately classify misinformation and explain its propagation. To address these challenges, we propose MCDF, a Multimodal Causal Detection Framework, integrating fusion-driven misinformation detection with causal analysis. Our framework consists of three key components: (1) a multimodal rumor detection module, which employs Graph Convolutional Networks (GCNs) for social interaction modeling, Vision Transformers (ViTs) for visual feature extraction, and RoBERTa for text-caption encoding, dynamically aligned via Tensor Fusion Networks (TFNs); (2) a Noise-Gating Mechanism, which refines feature alignment by filtering misleading or redundant inputs, ensuring robust misinformation classification; and (3) DEMATEL, a causal inference module that quantifies misinformation drivers, bridging misinformation classification with explainability. We evaluate our model on Twitter (X), FakeNewsNet (GossipCO and PolitiFact), and a curated Ghana-specific election dataset, demonstrating state-of-the-art performance in both classification and causal inference. MCDF offers a practical and interpretable framework for combating misinformation in real-world political communication, providing actionable insights for electoral stakeholders, fact-checkers, and social media analysts.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"125 ","pages":"Article 103470"},"PeriodicalIF":14.7,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144548899","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}

引用次数: 0

Towards enhanced LLM pretraining: Dynamic checkpoint merging via generation quality 增强LLM预训练：通过生成质量进行动态检查点合并

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-07-02 DOI: 10.1016/j.inffus.2025.103415

Zecheng Wang , Deyuan Liu , Chunshan Li , Dianhui Chu , Weipeng Chen , Bingning Wang , Dianbo Sui

{"title":"Towards enhanced LLM pretraining: Dynamic checkpoint merging via generation quality","authors":"Zecheng Wang , Deyuan Liu , Chunshan Li , Dianhui Chu , Weipeng Chen , Bingning Wang , Dianbo Sui","doi":"10.1016/j.inffus.2025.103415","DOIUrl":"10.1016/j.inffus.2025.103415","url":null,"abstract":"<div><div>Large language models (LLMs) have achieved widespread success across a wide range of natural language processing (NLP) tasks. Pretraining is a foundational step in the LLM training process, where the model gains a general understanding of language by exposure to vast amounts of text data. However, pretraining LLM comes with high costs and significant impacts on energy consumption and the environment. For instance, the emissions generated by training GPT-3 are approximately 552 net <span><math><mrow><mi>t</mi><mi>C</mi><msup><mrow><mi>O</mi></mrow><mrow><mn>2</mn></mrow></msup><mi>e</mi></mrow></math></span>. To alleviate this issue, we propose a simple and cost-efficient information fusion method, which involves merging the LLM’s checkpoints that share training trajectories during the pretraining phase. Additionally, previous model merging methods mostly maximize the posterior approximation of the model on the target dataset, or average the model parameters. The former often performs poorly in out-of-distribution settings, overlooking the fact that the target dataset is typically unlabeled, while the latter may get trapped in local minima. In this paper, we propose a method that uses generation quality as an indicator to determine merging weights. By calculating the perplexity of the LLM on the data, we can assess the learning degree of different checkpoints on the target dataset, thereby determining the merging weights effectively. Our method avoids overfitting the posterior distribution of the target dataset and relaxes the requirement for labeled information. Extensive experimental results demonstrate that our method consistently achieves more stable and superior overall performance in both in-distribution and out-of-distribution settings.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"125 ","pages":"Article 103415"},"PeriodicalIF":14.7,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597242","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}

引用次数: 0

Marginal distributionally robust fusion of probability density functions 概率密度函数的边际分布鲁棒融合

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-07-02 DOI: 10.1016/j.inffus.2025.103423

Yang Jiao , Dunbiao Niu , Yiguang Hong

{"title":"Marginal distributionally robust fusion of probability density functions","authors":"Yang Jiao , Dunbiao Niu , Yiguang Hong","doi":"10.1016/j.inffus.2025.103423","DOIUrl":"10.1016/j.inffus.2025.103423","url":null,"abstract":"<div><div>In this paper, we propose a novel fusion problem of probability density functions (PDFs) in a multi-agent system with several agents connected to a fusion center, where each agent has a random local observation vector statistically related to a random target state vector. The main goal of our fusion problem is to obtain a global state-observation PDF of the target state and all local observations at the fusion center under the condition that each agent transmits only a nominal local state-observation Gaussian PDF to the fusion center. In contrast to the previous methods of fusing PDFs, we consider the fusion of the global state-observation PDF as part of a statistical estimation task and develop a marginal distributionally robust fusion (MDRF) method to tackle our proposed problem. The MDRF method is designed based on a zero-sum game between a fictitious statistician choosing an estimator and the fusion center choosing a PDF in a candidate set, where the candidate set is defined by constraining a weighted average of Kullback–Leibler (KL) divergences between the local state-observation PDFs and the corresponding marginal candidate PDFs bounded within a fixed threshold. We not only prove that there exists a Gaussian distribution and an affine estimator constituting a global Nash equilibrium (NE) of the zero-sum game but also demonstrate that the global NE can be derived from a Karush–Kuhn–Tucker (KKT) point of a tractable convex optimization problem. Moreover, we develop a scheme based on the alternating direction method of multipliers (ADMM) algorithm for the tractable convex optimization problem and analyze the convergence performance of our algorithm for this optimization problem to ensure that the algorithm can converge to the KKT point. Finally, numerical experiments verify the effectiveness of our method.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"125 ","pages":"Article 103423"},"PeriodicalIF":14.7,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144569944","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}

引用次数: 0