Information Fusion最新文献

An interpretable integration fusion time-frequency prototype contrastive learning for machine fault diagnosis with limited labeled samples 基于可解释积分融合时频原型对比学习的有限标记样本机器故障诊断

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-05-29 DOI: 10.1016/j.inffus.2025.103340

Yutong Dong, Hongkai Jiang, Xin Wang, Mingzhe Mu

{"title":"An interpretable integration fusion time-frequency prototype contrastive learning for machine fault diagnosis with limited labeled samples","authors":"Yutong Dong, Hongkai Jiang, Xin Wang, Mingzhe Mu","doi":"10.1016/j.inffus.2025.103340","DOIUrl":"10.1016/j.inffus.2025.103340","url":null,"abstract":"<div><div>The rise of Industry 4.0 and Industry 5.0, focusing on digital transformation and human-machine collaboration, has boosted the need for advanced fault diagnosis technologies. These must be interpretable to ensure industrial efficiency, reliability, and safety. However, current methods often rely on single-sensor information, require many labeled samples for training, and struggle to justify diagnostic decisions. These limitations reduce their effectiveness in real-world production environments. Aiming at these problems, this paper proposed an interpretable integration fusion time-frequency prototype contrastive learning (IIF-TFPCL) for machine fault diagnosis with limited labeled samples. First, a data-level fusion method based on integrated Gini coefficient entropy is designed to achieve credible fusion of multi-sensor signals while enhancing the fault characteristics of the fused signals. Second, an interpretable wavelet feature fusion convolutional transformer architecture is constructed to achieve interpretable fault extraction from faulty signals. Then, a dual dynamic pseudo-labeling selection strategy is devised to efficiently choose high-confidence unlabeled samples from the original imbalanced unlabeled data. In this process, a self-attention mechanism is employed to measure the correlation between unlabeled samples and initial prototypes. Finally, a time-frequency prototype contrastive loss is constructed to enhance the discriminative ability and robustness of the network in fault diagnosis tasks. The IIF-TFPCL was validated using fused multi-sensor signals and various original single-sensor signals. The experiments display that it is significantly superior to the remaining seven comparison methods. The experimental analysis demonstrates the excellent fault identification performance and interpretability of the IIF-TFPCL with limited labeled data.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"124 ","pages":"Article 103340"},"PeriodicalIF":14.7,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144166605","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

Dynamic frequency selection and spatial interaction fusion for robust person search 鲁棒人物搜索的动态频率选择与空间交互融合

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-05-28 DOI: 10.1016/j.inffus.2025.103314

Qixian Zhang , Duoqian Miao , Qi Zhang , Cairong Zhao , Hongyun Zhang , Ye Sun , Ruizhi Wang

{"title":"Dynamic frequency selection and spatial interaction fusion for robust person search","authors":"Qixian Zhang , Duoqian Miao , Qi Zhang , Cairong Zhao , Hongyun Zhang , Ye Sun , Ruizhi Wang","doi":"10.1016/j.inffus.2025.103314","DOIUrl":"10.1016/j.inffus.2025.103314","url":null,"abstract":"<div><div>Person search aims to locate target individuals in large image databases captured by multiple non-overlapping cameras. Existing models primarily rely on spatial feature extraction to capture fine-grained local details, which is vulnerable to background clutter and occlusions and leads to unstable feature representations. To address the issues, we propose a Dynamic Frequency Selection and Spatial Interaction Fusion Network (PS-DFSI), marking the first attempt to introduce frequency decoupling and selection into person search. By integrating frequency and spatial features, PS-DFSI enhances feature expressiveness and robustness. Specifically, it comprises two core modules: the Dynamic Frequency Selection Module (DFSM) and the Spatial Frequency Interaction Module (SFIM). DFSM decouples feature maps into low-frequency and high-frequency components using learnable low-pass and high-pass filters, and a frequency selection modulator emphasizes key frequency components via channel attention. SFIM refines local details by fusing frequency-enhanced features with high-level semantic representations, leveraging multi-scale receptive fields and cross-feature attention for efficient spatial-frequency integration. Extensive experiments on CUHK-SYSU and PRW demonstrate that PS-DFSI significantly improves person search performance, validating its effectiveness and robustness.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"124 ","pages":"Article 103314"},"PeriodicalIF":14.7,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144166768","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

A knowledge-informed dynamic correlation modeling framework for lane-level traffic flow prediction 基于知识的车道级交通流预测动态关联建模框架

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-05-28 DOI: 10.1016/j.inffus.2025.103327

Ruiyuan Jiang , Shangbo Wang , Wei Ma , Yuli Zhang , Pengfei Fan , Dongyao Jia

{"title":"A knowledge-informed dynamic correlation modeling framework for lane-level traffic flow prediction","authors":"Ruiyuan Jiang , Shangbo Wang , Wei Ma , Yuli Zhang , Pengfei Fan , Dongyao Jia","doi":"10.1016/j.inffus.2025.103327","DOIUrl":"10.1016/j.inffus.2025.103327","url":null,"abstract":"<div><div>Lane-level traffic prediction forecasts near-future conditions at specific lane segments, enabling real-time traffic management and particularly aiding autonomous vehicles (AVs) in precise tasks such as car-following and lane changes. Despite substantial advancements in this field, some key challenges remain. First, the traffic state of a lane segment exhibits dynamic, nonlinear spatial correlation with other segments, making accurate modeling complex in real-world environments. Second, existing deep learning models depend heavily on specific datasets, leading to poor generalization. Third, while recent studies have shown that Large Language Models (LLMs) exhibit superior performance in generating reliable traffic prediction results, their direct application is hindered by inefficiency, high computational costs, and difficulties in capturing dynamic traffic features. To address these challenges, we propose the Knowledge-informed Dynamic Correlation Modeling (KIDCM) framework, which integrates pre-trained LLMs with traditional predictive methodologies to achieve a balance between generalization and prediction accuracy. Specifically, we introduce a General Spatial Dynamics Modeling (GSDM) method, which leverages the unbiased traffic data generated by LLM to analyze the general law dynamic spatial correlations. By integrating traditional time-series models with attention mechanisms, GSDM effectively models both linear temporal dependencies and nonlinear spatial interactions, ensuring robust generalization across varying conditions. Additionally, we develop a surrogate model that distills the traffic prediction function of LLMs. This surrogate model can be fine-tuned with small sample sizes, preserving the generalization advantages of LLMs while mitigating their typically high resource demands. Extensive evaluations demonstrate that our framework outperforms state-of-the-art models in terms of generalization, small-sample training, and computational cost.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"124 ","pages":"Article 103327"},"PeriodicalIF":14.7,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144166606","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

Mixed-noise robust tensor multi-view clustering via adaptive dictionary learning 基于自适应字典学习的混合噪声鲁棒张量多视图聚类

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-05-27 DOI: 10.1016/j.inffus.2025.103322

Jing-Hua Yang , Yi Zhou , Lefei Zhang , Heng-Chao Li

{"title":"Mixed-noise robust tensor multi-view clustering via adaptive dictionary learning","authors":"Jing-Hua Yang , Yi Zhou , Lefei Zhang , Heng-Chao Li","doi":"10.1016/j.inffus.2025.103322","DOIUrl":"10.1016/j.inffus.2025.103322","url":null,"abstract":"<div><div>Multi-view clustering (MVC) has received extensive attention by exploiting the consistent and complementary information among views. To improve the robustness of MVC, most MVC methods assume that the noise implicit in the data follows a predefined distribution. However, due to equipment limitations and transmission environment, the collected multi-view data often contains mixed noise. The predefined distribution assumption may not be able to effectively suppress complex mixed noise, resulting in a decrease in clustering performance. For solving the above problem, we propose a novel mixed-noise robust tensor multi-view clustering method (MRTMC) via adaptive dictionary learning. To accurately characterize the mixed noise, we consider mixed noise as a combination of structural noise and Gaussian noise and characterize both respectively. Specially, we design adaptive dictionary learning to accurately model structural noise containing semantic information and use Frobenius norm to constrain Gaussian noise. To fully mine the consistency among multiple views, we introduce a nonconvex tensor nuclear norm on the self-representation tensor to explore the high-order correlation among multiple views. Moreover, the weight of each view is learned through an adaptive weighting strategy. For solving the model, we develop an effective algorithm based on the alternating direction method of multipliers (ADMM) framework and provide the convergence guarantee of the algorithm under mild conditions. Extensive experimental results on simulated and real-world datasets indicate the clustering performance of the proposed MRTMC method is superior to the compared methods.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"123 ","pages":"Article 103322"},"PeriodicalIF":14.7,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144166634","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

A novel graph model for resolving power-asymmetric conflicts: Application in hierarchical diagnosis and treatment systems 一种解决权力不对称冲突的新图模型：在分层诊疗系统中的应用

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-05-27 DOI: 10.1016/j.inffus.2025.103310

Guolin Tang , Tangzhu Zhang , Francisco Chiclana , Peide Liu

{"title":"A novel graph model for resolving power-asymmetric conflicts: Application in hierarchical diagnosis and treatment systems","authors":"Guolin Tang , Tangzhu Zhang , Francisco Chiclana , Peide Liu","doi":"10.1016/j.inffus.2025.103310","DOIUrl":"10.1016/j.inffus.2025.103310","url":null,"abstract":"<div><div>As Chinese society undergoes rapid aging and urbanization, the existing medical service system faces significant challenges, including unequal resource distribution, a shortage of high-quality resources, and inefficient allocation. To address these issues, the hierarchical diagnosis and treatment system (HDTS) has been introduced to optimize medical resource allocation and utilization. However, implementing HDTS encounters complex conflicts of interest among multiple decision-makers (DMs), compounded by ambiguity, uncertainty, and power asymmetry. This paper proposes the power-asymmetric additive graph model for conflict resolution (PAAGMCR), a versatile tool that integrates qualitative and quantitative methods to address stakeholder conflict in HDTS implementation in Shandong, China. The optimal solution <span><math><msub><mrow><mi>s</mi></mrow><mrow><mn>18</mn></mrow></msub></math></span> can be identified using PAAGMCR: the Shandong Provincial Government should standardize medical treatment processes in tertiary hospitals, invest in grassroots medical facilities, allocate funds for public awareness campaigns, and encourage patients to seek initial treatment at the grassroots level. Tertiary hospitals should collaborate with grassroots hospitals to utilize subsidies for equipment upgrades and workforce training. Patients and their families should adhere to HDTS principles and make informed healthcare decisions. Furthermore, this study outlines an evolutionary path from the initial to the optimal state, offering theoretical support for resolving real-world conflicts. Finally, strategic recommendations are provided according to the analysis result of the conflict to guide DMs in implementing HDTS effectively.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"123 ","pages":"Article 103310"},"PeriodicalIF":14.7,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144166635","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

Low-rank tucker decomposition for multi-view outlier detection based on meta-learning 基于元学习的多视图异常点检测的低秩tucker分解

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-05-23 DOI: 10.1016/j.inffus.2025.103313

Wei Lin , Kun Xie , Jiayin Li , Shiping Wang , Li Xu

{"title":"Low-rank tucker decomposition for multi-view outlier detection based on meta-learning","authors":"Wei Lin , Kun Xie , Jiayin Li , Shiping Wang , Li Xu","doi":"10.1016/j.inffus.2025.103313","DOIUrl":"10.1016/j.inffus.2025.103313","url":null,"abstract":"<div><div>The analysis and mining of multi-view data have gained widespread attention, making multi-view anomaly detection a prominent research area. Despite notable advancements in the performance of existing multi-view anomaly detection methods, they still face certain limitations. (1) The existing methods fail to fully leverage the low-rank structure of multi-view data, which results in a lack of necessary interpretability when uncovering the latent relationships between views. (2) In the recovery of the consensus structure, the current methods achieve this merely through a simple aggregation process, lacking in-depth exploration and interaction between the potential structures of each view. To address these challenges, we propose the <u>L</u>ow-<u>R</u>ank <u>T</u>ucker <u>D</u>ecomposition based on <u>M</u>eta-Learning (LRTDM) for multi-view outlier detection. First, the low-rank Tucker decomposition is employed to reveal the low-rank structure of the multi-view self-expressive tensor. The factor matrices and core tensor effectively preserve and encode the latent structure of each view. This structured representation can efficiently capture the potential shared features between views, allowing for a more refined analysis of each individual view. Furthermore, meta-learning is utilized to define the learning and fusion of view-specific latent features as a nested optimization problem, which is solved alternately using a two-layer optimization scheme. Finally, anomalies are detected through the consensus matrix recovered from the latent representations and the error matrix during the self-expressive tensor learning process. Extensive experiments conducted on five publicly available datasets demonstrate the effectiveness of our approach. The results show that our algorithm improves detection accuracy by 2% to 10% compared to state-of-the-art methods.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"123 ","pages":"Article 103313"},"PeriodicalIF":14.7,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130917","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

CertainTTA: Estimating uncertainty for test-time adaptation on medical image segmentation 医学图像分割测试时间自适应的不确定度估计

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-05-22 DOI: 10.1016/j.inffus.2025.103300

Xingbo Dong , Liwen Wang , Xingguo Lv , Xiaoyan Zhang , Hui Zhang , Bin Pu , Zhan Gao , Iman Yi Liao , Zhe Jin

{"title":"CertainTTA: Estimating uncertainty for test-time adaptation on medical image segmentation","authors":"Xingbo Dong , Liwen Wang , Xingguo Lv , Xiaoyan Zhang , Hui Zhang , Bin Pu , Zhan Gao , Iman Yi Liao , Zhe Jin","doi":"10.1016/j.inffus.2025.103300","DOIUrl":"10.1016/j.inffus.2025.103300","url":null,"abstract":"<div><div>Cross-site distribution shift in medical images is a major factor causing model performance degradation, significantly challenging the deployment of pre-trained semantic segmentation models for clinical adoption. In this paper, we propose a novel framework, CertainTTA, to maximally exploit a pretrained model for test time adaptation. Firstly, we leverage variational inference and innovatively construct a probabilistic source model by incorporating Gaussian priors on the network parameters of the pre-trained source model. A predictive posterior distribution is computed at test time for the target image, which is then used to estimate the uncertainty of the target prediction based on entropy measure. In the meantime, a novel adaptive score is also constructed to measure the source model uncertainty on its adaptability for a target image based on the mutual information between the target prediction and the target input. Both output uncertainty and model uncertainty are incorporated at test time, where the former is minimized against a low-frequency prompt which optimally reduces the domain shift at image level, and the latter is used to select the target prediction with the best model adaptability during the prompt optimization process. CertainTTA overcomes the weakness of existing entropy minimization methods where the latter becomes unreliable under biased target scenarios and tends to yield overconfident predictions. To the best of our knowledge, CertainTTA also serves as the first solution to trace model adaptability in a CTTA setting. We conduct TTA and CTTA experiments on three medical semantic segmentation benchmarks, achieving average <span><math><mrow><mi>D</mi><mi>S</mi><mi>C</mi></mrow></math></span> improvements of 2.94%, 4.06%, and 3.49% under the TTA scenario over the state-of-the-art method on the OD/OC, polyp, and MRI Prostate segmentation datasets, respectively.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"123 ","pages":"Article 103300"},"PeriodicalIF":14.7,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116562","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

Context-driven and sparse decoding for Remote Sensing Visual Grounding 上下文驱动和稀疏解码的遥感视觉接地

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-05-22 DOI: 10.1016/j.inffus.2025.103296

Yichen Zhao , Yaxiong Chen , Ruilin Yao , Shengwu Xiong , Xiaoqiang Lu

{"title":"Context-driven and sparse decoding for Remote Sensing Visual Grounding","authors":"Yichen Zhao , Yaxiong Chen , Ruilin Yao , Shengwu Xiong , Xiaoqiang Lu","doi":"10.1016/j.inffus.2025.103296","DOIUrl":"10.1016/j.inffus.2025.103296","url":null,"abstract":"<div><div>Remote Sensing Visual Grounding (RSVG) is an emerging multimodal RS task that involves grounding textual descriptions to specific objects in remote sensing images. Previous methods often overlook the impact of complex backgrounds and similar geographic entities during feature extraction, which may confuse target features and cause performance bottlenecks. Moreover, remote sensing scenes include extensive surface information, much of which contributes little to the reasoning of the target object. This redundancy not only increases the computational burden but also impairs decoding efficiency. To this end, we propose the Context-driven Sparse Decoding Network (CSDNet) for accurate grounding through multimodal context-aware feature extraction and text-guided sparse reasoning. To alleviate target feature confusion, a Text-aware Fusion Module (TFM) is introduced to refine the visual features using textual cues related to the image context. In addition, a Context-enhanced Interaction Module (CIM) is proposed to harmonize the differences between remote sensing images and text by modeling multimodal contexts. To tackle surface information redundancy, a Text-guided Sparse Decoder (TSD) is developed, which decouples image resolution from reasoning complexity by performing sparse sampling under text guidance. Extensive experiments on DIOR-RSVG, OPT-RSVG, and VRSBench benchmarks demonstrate the effectiveness of CSDNet. Remarkably, CSDNet utilizes only 5.12% of the visual features in performing cross-modal reasoning about the target object. The code is available at <span><span>https://github.com/WUTCM-Lab/CSDNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"123 ","pages":"Article 103296"},"PeriodicalIF":14.7,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144166560","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

Distributed privacy-preserving keyword querying for integrated data in IoT networks via function secret sharing 基于功能秘密共享的物联网集成数据分布式保隐私关键字查询

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-05-22 DOI: 10.1016/j.inffus.2025.103298

Wei Shao , Lianhai Wang , Chunfu Jia , Qizheng Wang , Jinpeng Wang , Shujiang Xu , Shuhui Zhang , Mingyue Li

{"title":"Distributed privacy-preserving keyword querying for integrated data in IoT networks via function secret sharing","authors":"Wei Shao , Lianhai Wang , Chunfu Jia , Qizheng Wang , Jinpeng Wang , Shujiang Xu , Shuhui Zhang , Mingyue Li","doi":"10.1016/j.inffus.2025.103298","DOIUrl":"10.1016/j.inffus.2025.103298","url":null,"abstract":"<div><div>The growing adoption of IoT applications underscores the need for advanced data fusion and information acquisition techniques, driving demand for secure, privacy-preserving querying of integrated IoT data. Existing schemes like searchable encryption are practical but leak access patterns, while leakage-free methods using Oblivious RAM or cryptographic techniques incur significant resource overhead. In this paper, we propose PQBL, a framework for privacy-preserving, trusted data integration and search, leveraging distributed trust against malicious attackers. Our query scheme combines function secret sharing and blockchain to enable efficient, privacy-preserving searches on encrypted IoT data. To improve search efficiency, we introduce a compressed RAMBO Bloom Filter for keyword trapdoors. Formal security analysis shows that PQBL leaks no search patterns and is secure against Privacy under Selective Chosen-Plaintext Attacks. Extensive experiments on the PQBL prototype validate its effectiveness and efficiency.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"123 ","pages":"Article 103298"},"PeriodicalIF":14.7,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116559","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

Multi-View Fusion Graph Attention Network for Multilabel Class Incremental Learning 多标签类增量学习的多视图融合图注意网络

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-05-22 DOI: 10.1016/j.inffus.2025.103309

Anhui Tan , Yu Wang , Wei-Zhi Wu , Weiping Ding , Jiye Liang

{"title":"Multi-View Fusion Graph Attention Network for Multilabel Class Incremental Learning","authors":"Anhui Tan , Yu Wang , Wei-Zhi Wu , Weiping Ding , Jiye Liang","doi":"10.1016/j.inffus.2025.103309","DOIUrl":"10.1016/j.inffus.2025.103309","url":null,"abstract":"<div><div>Multilabel Class-Incremental Learning (MLCIL) refers to a variant of class-incremental learning and multilabel learning where models are required to learn from images or data associated with multiple labels, and new sets of classes are introduced incrementally. However, most existing MLCIL methods tend to rely heavily on limited single-view features, which makes it challenging for them to effectively capture class-specific characteristics and the correlations between different labels. Furthermore, MLCIL faces difficulties related to both intra-class and inter-class imbalances, which arise from the varying frequencies of class occurrences during each incremental session. To address these issues, we propose a novel MLCIL model called the Multi-View Fusion Graph Attention Network (MVGAT). First, the MVGAT architecture includes a multi-view feature extraction module that fuses class node features from three different perspectives of images, effectively capturing both local and global class-specific information. Second, MVGAT introduces a multi-view attention fusion module that combines the multi-view class node features based on label correlations. Importantly, the attention fusion modules trained in previous learning sessions are preserved, helping to mitigate catastrophic forgetting by providing independent probability predictions for their respective learned classes. Additionally, MVGAT is equipped with a pseudo-label correction module to enhance the accuracy of pseudo-labels by integrating predictions from the current session with those from historical frozen attention fusion modules. Moreover, an asymmetric loss function has been developed to balance intra-class and inter-class performance by dynamically adjusting negative focus parameters based on class occurrence frequency. Finally, experimental results on benchmark datasets demonstrate that MVGAT outperforms existing state-of-the-art methods.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"123 ","pages":"Article 103309"},"PeriodicalIF":14.7,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138437","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