Information Fusion最新文献_第10页

FuseMeter: An efficient framework for generic per-flow traffic measurement FuseMeter：通用的每流流量测量的有效框架

IF 15.5 1区计算机科学

Information Fusion Pub Date : 2026-06-01 Epub Date: 2025-12-16 DOI: 10.1016/j.inffus.2025.104065

Yifan Han , He Huang , Yu-E Sun , Jia Liu , Yang Du , Shigang Chen

{"title":"FuseMeter: An efficient framework for generic per-flow traffic measurement","authors":"Yifan Han , He Huang , Yu-E Sun , Jia Liu , Yang Du , Shigang Chen","doi":"10.1016/j.inffus.2025.104065","DOIUrl":"10.1016/j.inffus.2025.104065","url":null,"abstract":"<div><div>Modern high-speed networks demand real-time traffic analysis, but existing per-flow measurement solutions are inefficient and lack the generality to handle diverse, simultaneous tasks. They rely on deploying multiple dedicated algorithms, which leads to redundant computation and significant overhead given the stringent resource constraints of network processors. To solve this, we propose <strong>FuseMeter</strong>, a <em>generic</em> per-flow measurement framework that concurrently supports heterogeneous tasks with varying types and definitions within a single framework. Our design employs <em>multi-object sampling</em> to adaptively manage resources for different task types and a <em>hypercube counter</em> to efficiently integrate multi-dimensional records. This unified pipeline enables fast, flexible recovery of flow statistics across all deployed tasks. We prototype FuseMeter on a programmable hardware platform and provide configurable probabilistic guarantees for its performance. Experiments on real-world Internet traces demonstrate that FuseMeter outperforms state-of-the-art benchmarks. It reduces estimation errors by up to 95.41 %, cuts memory overhead by 70.66 %, and achieves a processing speed 12.39 times faster than existing methods.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104065"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145842586","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

Scoping review of multimodal sentiment analysis and summarization: State of the art, challenges and future directions 多模态情感分析与总结的范围综述：现状、挑战和未来方向

IF 15.5 1区计算机科学

Information Fusion Pub Date : 2026-06-01 Epub Date: 2025-12-27 DOI: 10.1016/j.inffus.2025.104082

Magaly Lika Fujimoto, Ricardo Marcondes Marcacini, Solange Oliveira Rezende

引用次数: 0

U2ENet: Unified End-to-End Network for Monocular Image-based Single-object and Multi-object 3D Visual Grounding U2ENet：基于单目图像的单目标和多目标三维视觉接地的统一端到端网络

IF 15.5 1区计算机科学

Information Fusion Pub Date : 2026-06-01 DOI: 10.1016/j.inffus.2026.104515

Keyu Guo , Hongkai Wei , Yongle Huang , Hongli Hu , Xiangyu Song , Shijie Sun , Yi Zhou , Huansheng Song , Nicola Strisciuglio

{"title":"U2ENet: Unified End-to-End Network for Monocular Image-based Single-object and Multi-object 3D Visual Grounding","authors":"Keyu Guo , Hongkai Wei , Yongle Huang , Hongli Hu , Xiangyu Song , Shijie Sun , Yi Zhou , Huansheng Song , Nicola Strisciuglio","doi":"10.1016/j.inffus.2026.104515","DOIUrl":"10.1016/j.inffus.2026.104515","url":null,"abstract":"<div><div>Human linguistic abilities and stereoscopic vision play crucial roles in understanding the world. 3D Visual Grounding (3DVG), serving as a bridge between these two fundamental human capacities, has been proposed to advance the 3D perception capabilities of artificial intelligence for comprehending real-world scenes. Existing 3DVG methods primarily depend on point cloud data as input and employ a two-stage paradigm that first detects candidate objects and then matches them with corresponding textual descriptions for single-object grounding. This limits their applicability in scenes requiring multi-object understanding and hinders deployment in resource-constrained environments. To this end, we introduce the Rope3DRefer, a novel large-scale dataset for unified single-object and multi-object 3DVG in monocular images. Furthermore, we propose U2ENet, the end-to-end network architecture for monocular image-based multi-object 3DVG. Specifically, we propose the Hierarchical Semantics-Aware Fusion (HSAF) procedure, which utilizes multi-level semantic representations from State Space Duality outputs as cues. Through Semantic-Aware Mamba Fusion, HSAF achieves effective interaction with multi-scale feature maps to enhance their semantic awareness selectively. This provides superior perceptual capabilities for this task. Extensive experiments on the proposed Rope3DRefer demonstrate that our method achieves state-of-the-art performance, significantly outperforming modified point-cloud-based and existing monocular methods across various difficulty levels and evaluation metrics. Our dataset and code are available at <span><span>https://github.com/andreaklay/U2ENet</span><svg><path></path></svg></span></div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"136 ","pages":"Article 104515"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148191435","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

Survey of uncertainty estimation in LLMs - Sources, methods, applications, and challenges 法学硕士不确定性评估综述——来源、方法、应用和挑战

IF 15.5 1区计算机科学

Information Fusion Pub Date : 2026-06-01 Epub Date: 2025-12-19 DOI: 10.1016/j.inffus.2025.104057

Jianfeng He , Linlin Yu , Changbin Li , Runing Yang , Fanglan Chen , Kangshuo Li , Min Zhang , Shuo Lei , Xuchao Zhang , Mohammad Beigi , Kaize Ding , Bei Xiao , Lifu Huang , Feng Chen , Ming Jin , Chang-Tien Lu

{"title":"Survey of uncertainty estimation in LLMs - Sources, methods, applications, and challenges","authors":"Jianfeng He , Linlin Yu , Changbin Li , Runing Yang , Fanglan Chen , Kangshuo Li , Min Zhang , Shuo Lei , Xuchao Zhang , Mohammad Beigi , Kaize Ding , Bei Xiao , Lifu Huang , Feng Chen , Ming Jin , Chang-Tien Lu","doi":"10.1016/j.inffus.2025.104057","DOIUrl":"10.1016/j.inffus.2025.104057","url":null,"abstract":"<div><div>Large Language Models (LLMs) have demonstrated exceptional performance across a wide range of domains. However, inaccuracies in their outputs can lead to severe consequences in high-stakes areas such as finance and healthcare, where errors may result in the loss of money, time, or even lives. As a result, recent research has increasingly focused on uncertainty estimation in LLMs, aiming to quantify the trustworthiness of model-generated content given specific inputs. Despite this growing interest, the sources of uncertainty in LLMs remain insufficiently understood. As a result, this survey provides a comprehensive overview of uncertainty estimation for LLMs from the perspective of uncertainty sources, serving as a foundational resource for researchers entering the field. We begin by reviewing essential background on LLMs, followed by a detailed clarification of uncertainty sources relevant to them. We then introduce various uncertainty estimation methods, including both commonly used and LLM-specific approaches. Metrics for evaluating uncertainty are discussed, along with key application areas. Finally, we highlight major challenges and outline future research directions aimed at improving the trustworthiness and reliability of LLMs.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104057"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785022","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

TPIN: Text-based parallel interaction network with modality-common and modality-specific for multimodal sentiment analysis 基于多情态情感分析的情态通用和情态特定的文本并行交互网络

IF 15.5 1区计算机科学

Information Fusion Pub Date : 2026-06-01 Epub Date: 2025-12-24 DOI: 10.1016/j.inffus.2025.104087

Changbin Wang, Fengrui Ji, Baolin Liu

{"title":"TPIN: Text-based parallel interaction network with modality-common and modality-specific for multimodal sentiment analysis","authors":"Changbin Wang, Fengrui Ji, Baolin Liu","doi":"10.1016/j.inffus.2025.104087","DOIUrl":"10.1016/j.inffus.2025.104087","url":null,"abstract":"<div><div>Learning an effective joint representation is fundamental for Multimodal Sentiment Analysis (MSA). Existing studies typically adopt complex networks to construct joint multimodal representations directly, yet often overlook the heterogeneity among different modalities as well as the preservation of modality-specific information. Moreover, current methods tend to treat all modalities equally, failing to exploit the rich emotional cues in the text modality. To address these issues, we propose a Text-based Parallel Interaction Network (TPIN) that aims to trade off the commonality and specificity of different modalities. The TPIN consists of two components: Modality-Common Information Processing (MCIP) and Modality-Specific Information Processing (MSIP). In MCIP, we innovatively propose a contrastive learning algorithm with Hard Negative Mining (HNM), which is integrated into our designed Two-Stage Contrastive Learning (TSCL) to mitigate inter-modal heterogeneity. Additionally, we design a Text-Guided Dynamic Semantic Aggregation (TG-DSA) module to enable deep multimodal fusion under the guidance of text modality. In MSIP, we devise a dynamic routing mechanism, which iteratively optimizes routing weights to better capture modality-specific information in visual and acoustic modalities. Experimental results demonstrate that our method achieves state-of-the-art performance on both the CMU-MOSI and CMU-MOSEI datasets, showing consistent gains of 0.5%–1.2% across major evaluation metrics compared with recent advanced models.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104087"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145822967","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

Rethink: reveal the impact of semantic distribution transfer from the cross-modal hashing perspective 重新思考：从跨模态散列的角度揭示语义分布转移的影响

IF 15.5 1区计算机科学

Information Fusion Pub Date : 2026-06-01 Epub Date: 2026-01-05 DOI: 10.1016/j.inffus.2026.104123

Yinan Li , Zhi Liu , Jiajun Tang , Binghong Chen , Mingjin Kuai , Jun Long , Zhan Yang

{"title":"Rethink: reveal the impact of semantic distribution transfer from the cross-modal hashing perspective","authors":"Yinan Li , Zhi Liu , Jiajun Tang , Binghong Chen , Mingjin Kuai , Jun Long , Zhan Yang","doi":"10.1016/j.inffus.2026.104123","DOIUrl":"10.1016/j.inffus.2026.104123","url":null,"abstract":"<div><div>Hashing has been extensively applied in cross-modal retrieval by mapping diverse modalities data into binary codes. Semantic transfer aims to enhance the relevance of heterogeneous representations through migrating valuable information from one modality to another in the unsupervised paradigm. The combination of semantic transfer and hash learning substitutes the dense vector search with Hamming distance, significantly reducing storage requirements and increasing retrieval efficiency. However, the current unsupervised mechanism demonstrates ordinary performance in retrieval precision, which requires more improvement from semantic annotation. Particularly, the mediocre information fusion strategy directly affects the quality of learned hash codes. In this paper, we propose a novel <strong>S</strong>emantic <strong>T</strong>ransfer framework for <strong>S</strong>emi-supervised <strong>C</strong>ross-modal <strong>H</strong>ashing, denoted as STSCH. Initially, we utilize multiple auto-encoders to learn the high-level semantic representation of each modality. To guarantee the completeness of heterogeneous data, we incorporate them via semantic transfer and analyse the feature distribution of diverse modalities. Furthermore, an asymmetric hash learning framework between individual modality-specific representation and minor semantic labels is constructed. Finally, an effective optimization algorithm is proposed. Comprehensive experiments on Wiki, MIRFlickr, and NUS-WIDE datasets demonstrate the superior performance of STSCH to state-of-the-art hashing approaches.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104123"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145902475","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

Multimodal brain network analysis: Research advances and challenges 多模态脑网络分析：研究进展与挑战

IF 15.5 1区计算机科学

Information Fusion Pub Date : 2026-06-01 Epub Date: 2025-12-26 DOI: 10.1016/j.inffus.2025.104096

Tao Hou , Wenhao Dai , Jiashuang Huang , Youyong Kong , Weiping Ding

{"title":"Multimodal brain network analysis: Research advances and challenges","authors":"Tao Hou , Wenhao Dai , Jiashuang Huang , Youyong Kong , Weiping Ding","doi":"10.1016/j.inffus.2025.104096","DOIUrl":"10.1016/j.inffus.2025.104096","url":null,"abstract":"<div><div>Brain network analysis has become a powerful approach in neuroscience for characterizing the structural and functional organization of brain regions. Advances in neuroimaging techniques, including structural magnetic resonance imaging (sMRI), functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and electroencephalography (EEG), provide complementary information on brain anatomy, connectivity patterns, and electrophysiological dynamics across different temporal and spatial scales. While unimodal analyses provide valuable insights, they are inherently limited in capturing the complexity and dynamics of brain networks. Multimodal fusion strategies have therefore become crucial for constructing more accurate and informative brain network models, enabling applications such as neurological disorder classification, brain age prediction, and cognitive function assessment. In this review, we systematically survey contemporary deep learning architectures for brain network analysis, including traditional frameworks and emerging technologies such as large language models. We further review multimodal fusion strategies, highlighting recent methodological advances, their relevance to clinical and cognitive applications, and the role of interpretability in enhancing model understanding and revealing neural mechanisms. We also discuss open technical challenges and consider potential directions for future research, including strategies to address individual variability and to ensure data privacy and security in multimodal brain network modeling. This review provides a comprehensive and forward-looking reference for researchers seeking to advance the understanding and practical application of complex brain networks.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104096"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145845114","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

SynJAC: synthetic-data-driven joint-granular adaptation and calibration for domain specific scanned document key information extraction 面向特定领域扫描文档关键信息提取的综合数据驱动的关节颗粒自适应与校准

IF 15.5 1区计算机科学

Information Fusion Pub Date : 2026-06-01 Epub Date: 2025-12-23 DOI: 10.1016/j.inffus.2025.104074

Yihao Ding , Soyeon Caren Han , Zechuan Li , Hyunsuk Chung

{"title":"SynJAC: synthetic-data-driven joint-granular adaptation and calibration for domain specific scanned document key information extraction","authors":"Yihao Ding , Soyeon Caren Han , Zechuan Li , Hyunsuk Chung","doi":"10.1016/j.inffus.2025.104074","DOIUrl":"10.1016/j.inffus.2025.104074","url":null,"abstract":"<div><div>Visually Rich Documents (VRDs), comprising elements such as charts, tables, and paragraphs, convey complex information across diverse domains. However, extracting key information from these documents remains labour-intensive, particularly for scanned formats with inconsistent layouts and domain-specific requirements. Despite advances in pretrained models for VRD understanding, their dependence on large annotated datasets for fine-tuning hinders scalability. This paper proposes <strong>SynJAC</strong> (Synthetic-data-driven Joint-granular Adaptation and Calibration), a method for key information extraction in scanned documents. SynJAC leverages synthetic, machine-generated data for domain adaptation and employs calibration on a small, manually annotated dataset to mitigate noise. By integrating fine-grained and coarse-grained document representation learning, SynJAC significantly reduces the need for extensive manual labelling while achieving competitive performance. Extensive experiments demonstrate its effectiveness in domain-specific and scanned VRD scenarios.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104074"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145822974","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

ViP-HMNN: a visual pathway-inspired hybrid neural network incorporated with in-memory computing for object recognition ViP-HMNN：一种结合内存计算的视觉路径启发的混合神经网络，用于对象识别

IF 15.5 1区计算机科学

Information Fusion Pub Date : 2026-06-01 Epub Date: 2025-12-22 DOI: 10.1016/j.inffus.2025.104086

Xiaoyue Ji , Yifeng Han , Chun Sing Lai , Fangwen Yu , Zhekang Dong

{"title":"ViP-HMNN: a visual pathway-inspired hybrid neural network incorporated with in-memory computing for object recognition","authors":"Xiaoyue Ji , Yifeng Han , Chun Sing Lai , Fangwen Yu , Zhekang Dong","doi":"10.1016/j.inffus.2025.104086","DOIUrl":"10.1016/j.inffus.2025.104086","url":null,"abstract":"<div><div>The integration of artificial neural networks (ANNs) and spiking neural networks (SNNs) has significant potential for advancing artificial general intelligence (AGI). However, the hardware design of hybrid neural networks (HNNs) still primarily relies on near-memory computing architectures, which have yet to fully overcome the separation between processing and memory units. To address this, we develop a visual pathway-inspired hybrid memristive neural network (ViP-HMNN). Specifically, we design a general and compact memristor-based neuron circuit that can efficiently implement both ANN and SNN activation functions, serving as the core component of the proposed ViP-HMNN. To improve the understanding of the designed ViP-HMNN, a ventral pathway-inspired static feature extraction module (VP-SFEM), a dorsal pathway-inspired dynamic feature representation module (DP-DFRM), and a complementary feature fusion output module (CFFOM) are proposed. For validation, the proposed ViP-HMNN with synergistic hybrid training strategy is applied for object recognition. Compared to software-based object recognition methods, the proposed ViP-HMNN achieves software-compatible accuracy (ranking in the top three) and has a significant advantage in terms of time consumption (at least 8-fold faster). Compared to in-memory computing architectures, the proposed ViP-HMNN exhibits enhancements of up to 2.65× in area overhead, 1.83× in latency, and 1.87× in energy consumption. Compared to the RTX 3090 GPU, the proposed ViP-HMNN achieves a minimum of 1000× reduction in latency and 500× in energy saving.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104086"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145808543","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

MoMD Transformer: adaptive multi-modal fault diagnosis via knowledge transfer with vibration-current signals MoMD变压器：基于振动电流信号知识传递的自适应多模态故障诊断

IF 15.5 1区计算机科学

Information Fusion Pub Date : 2026-06-01 Epub Date: 2025-12-22 DOI: 10.1016/j.inffus.2025.104079

Xiaohan Zhang , Han Wang , Chenze Wang , Gaowei Xu , Min Liu , Chuang Liu

{"title":"MoMD Transformer: adaptive multi-modal fault diagnosis via knowledge transfer with vibration-current signals","authors":"Xiaohan Zhang , Han Wang , Chenze Wang , Gaowei Xu , Min Liu , Chuang Liu","doi":"10.1016/j.inffus.2025.104079","DOIUrl":"10.1016/j.inffus.2025.104079","url":null,"abstract":"<div><div>Fault diagnosis is crucial to ensure the reliable and safe operation of electromechanical systems in the manufacturing industry. Recently, multi-modal data fusion based fault diagnosis methods have achieved remarkable progress. However, these methods impose additional constraints on industrial scenarios because they require the simultaneous input of multi-modal data during the inference phase, which limits their applicability in cases where only single-modal data acquisition is feasible during operation. To overcome this limitation, a novel Mixture-of-Modality-Diagnosis (MoMD) Transformer is proposed in this paper for adaptive multi-modal fault diagnosis with vibration and current signals. In this model, we improve the feed-forward network in Transformer into a multi-channel structure to adaptively process multiple modalities. Additionally, to address the challenge of weak fault signatures in current modality, we design a global knowledge transfer module that utilizes vibration features to guide the feature learning for current signals. Specifically, our comprehensive analysis of feature alignment strategies reveals that the one-to-one paradigm is superior to contrastive learning paradigm for fault data, given its low intra-class variance. Moreover, to enhance the representation ability for time-series signals, the masked signal modeling task is introduced into the training phase, thereby improving the diagnostic accuracy. Experimental results on two datasets demonstrate that the proposed method achieves superior diagnostic accuracy, reaching 99.96 % and 100 % respectively, outperforming other methods in various modality-availability scenarios.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"130 ","pages":"Article 104079"},"PeriodicalIF":15.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823826","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