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学者姓名：郭文忠
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Multi-view learning has demonstrated strong potential in processing data from different sources or viewpoints. Despite the significant progress made by Multi-view Graph Neural Networks (MvGNNs) in exploiting graph structures, features, and representations, existing research generally lacks architectures specifically designed for the intrinsic properties of multi-view data. This leads to models that still have deficiencies in fully utilizing consistent and complementary information in multi-view data. Most of current research tends to simply extend the single-view GNN framework to multi-view data, lacking in-depth strategies to handle and leverage the unique properties of these data. To address this issue, we propose a simple yet effective MvGNN framework called Multi-view Representation Learning with Decoupled private and shared Propagation (MvRL-DP). This framework enables multi-view data to be effectively processed as a whole by alternating private and shared operations to integrate cross-view information. In addition, to address possible inconsistencies between views, we present a discriminative loss that promotes class separability and prevents the model from being misled by noise hidden in multi-view data. Experiments demonstrate that the proposed framework is superior to current state-of-the-art methods in the multi-view semi-supervised classification task.
Keyword ：
Multi-view learning Multi-view learning Propagation decoupling Propagation decoupling Representation learning Representation learning Semi-supervised classification Semi-supervised classification Tensor operation Tensor operation
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| GB/T 7714 | Wang, Xuzheng , Lan, Shiyang , Wu, Zhihao et al. Multi-view Representation Learning with Decoupled private and shared Propagation [J]. | KNOWLEDGE-BASED SYSTEMS , 2025 , 310 . |
| MLA | Wang, Xuzheng et al. "Multi-view Representation Learning with Decoupled private and shared Propagation" . | KNOWLEDGE-BASED SYSTEMS 310 (2025) . |
| APA | Wang, Xuzheng , Lan, Shiyang , Wu, Zhihao , Guo, Wenzhong , Wang, Shiping . Multi-view Representation Learning with Decoupled private and shared Propagation . | KNOWLEDGE-BASED SYSTEMS , 2025 , 310 . |
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3D anomaly detection aims to solve the problem that image anomaly detection is greatly affected by lighting conditions. As commercial confidentiality and personal privacy become increasingly paramount, access to training samples is often restricted. To address these challenges, we propose a zero-shot 3D anomaly detection method. Unlike previous CLIP-based methods, the proposed method does not require any prompt and is capable of detecting anomalies on the depth modality. Furthermore, we also propose a pre-trained structural rerouting strategy, which modifies the transformer without retraining or fine-tuning for the anomaly detection task. Most importantly, this paper proposes an online voter mechanism that registers voters and performs majority voter scoring in a one-stage, zero-start and growth-oriented manner, enabling direct anomaly detection on unlabeled test sets. Finally, we also propose a confirmatory judge credibility assessment mechanism, which provides an efficient adaptation for possible few-shot conditions. Results on datasets such as MVTec3D-AD demonstrate that the proposed method can achieve superior zero-shot 3D anomaly detection performance, indicating its pioneering contributions within the pertinent domain.
Keyword ：
Anomaly detection Anomaly detection Multimodal Multimodal Online voter mechanism Online voter mechanism Pretrained model Pretrained model Zero-shot Zero-shot
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| GB/T 7714 | Zheng, Wukun , Ke, Xiao , Guo, Wenzhong . Zero-shot 3D anomaly detection via online voter mechanism [J]. | NEURAL NETWORKS , 2025 , 187 . |
| MLA | Zheng, Wukun et al. "Zero-shot 3D anomaly detection via online voter mechanism" . | NEURAL NETWORKS 187 (2025) . |
| APA | Zheng, Wukun , Ke, Xiao , Guo, Wenzhong . Zero-shot 3D anomaly detection via online voter mechanism . | NEURAL NETWORKS , 2025 , 187 . |
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Asa promising area in machine learning, multi-view learning enhances model performance by integrating data from various views. With the rise of graph convolutional networks, many studies have explored incorporating them into multi-view learning frameworks. However, these methods often require storing the entire graph topology, leading to significant memory demands. Additionally, iterative update operations in graph convolutions lead to longer inference times, making it difficult to deploy existing multi-view learning models on large graphs. To overcome these challenges, we introduce an efficient multi-view graph convolutional network via local aggregation and global propagation. In the local aggregation module, we use a structure-aware matrix for feature aggregation, which significantly reduces computational complexity compared to traditional graph convolutions. After that, we design a global propagation module that allows the model to be trained in batches, enabling deployment on large-scale graphs. Finally, we introduce the attention mechanism into multi-view feature fusion to more effectively explore the consistency and complementarity between views. The proposed method is employed to perform multi-view semi-supervised classification, and comprehensive experimental results on benchmark datasets validate its effectiveness.
Keyword ：
Graph neural networks Graph neural networks Local aggregation Local aggregation Multi-view learning Multi-view learning Representation learning Representation learning Semi-supervised classification Semi-supervised classification
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| GB/T 7714 | Liu, Lu , Shi, Yongquan , Pi, Yueyang et al. Efficient multi-view graph convolutional networks via local aggregation and global propagation [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2025 , 266 . |
| MLA | Liu, Lu et al. "Efficient multi-view graph convolutional networks via local aggregation and global propagation" . | EXPERT SYSTEMS WITH APPLICATIONS 266 (2025) . |
| APA | Liu, Lu , Shi, Yongquan , Pi, Yueyang , Guo, Wenzhong , Wang, Shiping . Efficient multi-view graph convolutional networks via local aggregation and global propagation . | EXPERT SYSTEMS WITH APPLICATIONS , 2025 , 266 . |
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Robust nonlinear regression frequently arises in data analysis that is affected by outliers in various application fields such as system identification, signal processing, and machine learning. However, it is still quite challenge to design an efficient algorithm for such problems due to the nonlinearity and nonsmoothness. Previous researches usually ignore the underlying structure presenting in the such nonlinear regression models, where the variables can be partitioned into a linear part and a nonlinear part. Inspired by the high efficiency of variable projection algorithm for solving separable nonlinear least squares problems, in this paper, we develop a robust variable projection (RoVP) method for the parameter estimation of separable nonlinear regression problem with $L_{1}$ norm loss. The proposed algorithm eliminates the linear parameters by solving a linear programming subproblem, resulting in a reduced problem that only involves nonlinear parameters. More importantly, we derive the Jacobian matrix of the reduced objective function, which tackles the coupling between the linear parameters and nonlinear parameters. Furthermore, we observed an intriguing phenomenon in the landscape of the original separable nonlinear objective function, where some narrow valleys frequently exist. The RoVP strategy can effectively diminish the likelihood of the algorithm getting stuck in these valleys and accelerate its convergence. Numerical experiments confirm the effectiveness and robustness of the proposed algorithm. IEEE
Keyword ：
Autoregressive processes Autoregressive processes Jacobian matrices Jacobian matrices Linear programming Linear programming Optimization Optimization Parameter estimation Parameter estimation Predictive models Predictive models radial basis function network based autoregressive (RBF-AR) model radial basis function network based autoregressive (RBF-AR) model robust parameter estimation robust parameter estimation Signal processing algorithms Signal processing algorithms System identification System identification variable projection (VP) algorithm variable projection (VP) algorithm
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| GB/T 7714 | Chen, G. , Su, X. , Gan, M. et al. Robust variable projection algorithm for the identification of separable nonlinear models [J]. | IEEE Transactions on Automatic Control , 2024 , 69 (9) : 1-8 . |
| MLA | Chen, G. et al. "Robust variable projection algorithm for the identification of separable nonlinear models" . | IEEE Transactions on Automatic Control 69 . 9 (2024) : 1-8 . |
| APA | Chen, G. , Su, X. , Gan, M. , Guo, W. , Chen, C.L.P. . Robust variable projection algorithm for the identification of separable nonlinear models . | IEEE Transactions on Automatic Control , 2024 , 69 (9) , 1-8 . |
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Global routing is an extremely important stage of very large scale integration (VLSI) physical design. With the rise of nano-scale integrated circuit design, the multilayer global routing problem has attracted considerable research interest during the past few years. In this article, a multilayer X-architecture global routing (ML-XGR) system based on particle swarm optimization (PSO), called FZU-Router, is proposed to solve the ML-XGR problem for the first time. FZU-Router contains a multilayer X-architecture integer linear programming (MX-ILP) model and a multilayer X-architecture PSO (MX-PSO) algorithm, which are presented to formulate and solve the ML-XGR problem, respectively. Moreover, four effective strategies are designed to enhance the efficiency of FZU-Router: 1) a strategy for generating new routing modes is proposed to strengthen the robustness of encoding strategy of MX-PSO; 2) a strategy for combining MX-PSO with maze routing is proposed to improve the routability; 3) a strategy for reducing the channel capacity is proposed to make better use of optimization ability of MX-PSO; and 4) a strategy for dynamic resource assignment is proposed to make better use of routing resources and shorten the running time. Experimental results on multiple benchmarks confirm that the proposed FZU-Router leads to fewer total overflow and shorter total wirelength compared with the state-of-the-art routers. IEEE
Keyword ：
Global routing Global routing integer linear programming (ILP) integer linear programming (ILP) Integrated circuit interconnections Integrated circuit interconnections multilayer routing multilayer routing Nonhomogeneous media Nonhomogeneous media Optimization Optimization particle swarm optimization (PSO) particle swarm optimization (PSO) Partitioning algorithms Partitioning algorithms Routing Routing Very large scale integration Very large scale integration very large scale integration (VLSI) very large scale integration (VLSI) Wire Wire X-architecture X-architecture
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| GB/T 7714 | Liu, G. , Zhu, Y. , Zhuang, Z. et al. A Robust Multilayer X-Architecture Global Routing System Based on Particle Swarm Optimization [J]. | IEEE Transactions on Systems, Man, and Cybernetics: Systems , 2024 , 54 (9) : 1-14 . |
| MLA | Liu, G. et al. "A Robust Multilayer X-Architecture Global Routing System Based on Particle Swarm Optimization" . | IEEE Transactions on Systems, Man, and Cybernetics: Systems 54 . 9 (2024) : 1-14 . |
| APA | Liu, G. , Zhu, Y. , Zhuang, Z. , Pei, Z. , Gan, M. , Huang, X. et al. A Robust Multilayer X-Architecture Global Routing System Based on Particle Swarm Optimization . | IEEE Transactions on Systems, Man, and Cybernetics: Systems , 2024 , 54 (9) , 1-14 . |
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Few-shot object detection achieves rapid detection of novel-class objects by training detectors with a minimal number of novel-class annotated instances. Transfer learning-based few-shot object detection methods have shown better performance compared to other methods such as meta-learning. However, when training with base-class data, the model may gradually bias towards learning the characteristics of each category in the base-class data, which could result in a decrease in learning ability during fine-tuning on novel classes, and further overfitting due to data scarcity. In this paper, we first find that the generalization performance of the base-class model has a significant impact on novel class detection performance and proposes a generalization feature extraction network framework to address this issue. This framework perturbs the base model during training to encourage it to learn generalization features and solves the impact of changes in object shape and size on overall detection performance, improving the generalization performance of the base model. Additionally, we propose a feature-level data augmentation method based on self-distillation to further enhance the overall generalization ability of the model. Our method achieves state-of-the-art results on both the COCO and PASCAL VOC datasets, with a 6.94% improvement on the PASCAL VOC 10-shot dataset. IEEE
Keyword ：
Adaptation models Adaptation models Computational modeling Computational modeling data augmentation data augmentation Data models Data models Feature extraction Feature extraction few-shot learning few-shot learning object detection object detection Object detection Object detection self-distillation self-distillation Shape Shape Training Training Transfer learning Transfer learning
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| GB/T 7714 | Ke, X. , Chen, Q. , Liu, H. et al. GFENet: Generalization Feature Extraction Network for Few-Shot Object Detection [J]. | IEEE Transactions on Circuits and Systems for Video Technology , 2024 , 34 (12) : 1-1 . |
| MLA | Ke, X. et al. "GFENet: Generalization Feature Extraction Network for Few-Shot Object Detection" . | IEEE Transactions on Circuits and Systems for Video Technology 34 . 12 (2024) : 1-1 . |
| APA | Ke, X. , Chen, Q. , Liu, H. , Guo, W. . GFENet: Generalization Feature Extraction Network for Few-Shot Object Detection . | IEEE Transactions on Circuits and Systems for Video Technology , 2024 , 34 (12) , 1-1 . |
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Graph convolutional networks have significant advantages in dealing with graph-structured data, but most existing methods usually potentially assume that nodes belonging to the same class in a graph tend to form edges, yet inter-class edges exist in many real-world graph-structured data. Due to the propagation mechanism of graph convolutional networks, it is challenging to prevent the interference aggregation from nodes of different classes, which may result in the incorporation of noise and irrelevant data in the outcome, ultimately decreasing the performance of the model. In this paper, we propose a new framework to address this issue on heterophilous graph-structured data. The proposed method comprises two main components. On one hand, the homophily of the graph-structured data is modeled so that the method can adaptively adjust the information propagation process according to the homophily of the edges, and mitigate the influence of inter-class information. On the other hand, the implicit node interaction is captured through the learned feature space, which is then fused with the original interaction to aggregate sufficient intra-class knowledge. Extensive experiments on real-world datasets demonstrate the superiority of the proposed method against current state-of-the-art approaches. © 2024 Elsevier B.V.
Keyword ：
Contrastive learning Contrastive learning Graph convolutional network Graph convolutional network Heterophilous graph Heterophilous graph Semi-supervised classification Semi-supervised classification
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| GB/T 7714 | Huang, Y. , Shi, Y. , Pi, Y. et al. Adaptive-propagating heterophilous graph convolutional network [J]. | Knowledge-Based Systems , 2024 , 302 . |
| MLA | Huang, Y. et al. "Adaptive-propagating heterophilous graph convolutional network" . | Knowledge-Based Systems 302 (2024) . |
| APA | Huang, Y. , Shi, Y. , Pi, Y. , Li, J. , Wang, S. , Guo, W. . Adaptive-propagating heterophilous graph convolutional network . | Knowledge-Based Systems , 2024 , 302 . |
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Camera-based stereo 3D object detection estimates 3D properties of objects with binocular images only, which is a cost-effective solution for autonomous driving. The state-of-the-art methods mainly improve the detection accuracy of general objects by designing ingenious stereo matching algorithms or complex pipeline modules. Moreover, additional fine-grained annotations, such as masks or LiDAR point clouds, are often introduced to deal with the occlusion problems, which brings in high manual costs for this task. To address the detection bottleneck caused by occlusion in a more cost-effective manner, we develop a novel stereo 3D object detection method named DSC3D, which achieves significant improvements for occluded objects without introducing additional supervision. Specifically, we first report the ambiguity in feature sampling, which refers to the presence of noisy features in the sampling for occluded objects. Then, we propose the Epipolar Constraint Deform-Attention (ECDA) module to address the unreliable left-right correspondence computation in stereo matching caused by occlusion, which reweights epipolar features by adaptively aggregating local neighbor information. Furthermore, to ensure that 3D property estimation is based on robust object features, we propose visible regions guided constraint to explicitly guide the offset learning for feature sampling. Extensive experiments conducted on the KITTI benchmark have demonstrated the proposed DSC3D outperforms the state-of-the-art camera-based methods. © 1991-2012 IEEE.
Keyword ：
3D Object Detection 3D Object Detection Autonomous Driving Autonomous Driving Binocular Images Binocular Images Occluded Object Occluded Object Stereo Matching Stereo Matching
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| GB/T 7714 | Chen, J. , Song, Q. , Guo, W. et al. DSC3D: Deformable Sampling Constraints in Stereo 3D Object Detection for Autonomous Driving [J]. | IEEE Transactions on Circuits and Systems for Video Technology , 2024 . |
| MLA | Chen, J. et al. "DSC3D: Deformable Sampling Constraints in Stereo 3D Object Detection for Autonomous Driving" . | IEEE Transactions on Circuits and Systems for Video Technology (2024) . |
| APA | Chen, J. , Song, Q. , Guo, W. , Huang, R. . DSC3D: Deformable Sampling Constraints in Stereo 3D Object Detection for Autonomous Driving . | IEEE Transactions on Circuits and Systems for Video Technology , 2024 . |
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In label distribution learning (LDL), an instance is involved with many labels in different importance degrees, and the feature space of instances is accompanied with thousands of redundant and/or irrelevant features. Therefore, the main characteristic of feature selection in LDL is to evaluate the ability of each feature. Motivated by neighborhood rough set (NRS), which can be used to measure the dependency degree of feature via constructing neighborhood relations on feature space and label space, respectively, this article proposes a novel label distribution feature selection method. In this article, the neighborhood class of instance in label distribution space is defined, which is beneficial to recognize the logical class of target instance. Then, a new NRS model for LDL is proposed. Specially, the dependency degree of feature combining label weight is defined. Finally, a label distribution feature selection based on NRS is presented. Extensive experiments on 12 data sets show the effectiveness of the proposed algorithm. © 2024 John Wiley & Sons Ltd.
Keyword ：
feature selection feature selection label ambiguity label ambiguity label distribution learning label distribution learning neighborhood rough set neighborhood rough set
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| GB/T 7714 | Wu, Y. , Guo, W. , Lin, Y. . Label distribution feature selection based on neighborhood rough set [J]. | Concurrency and Computation: Practice and Experience , 2024 , 36 (23) . |
| MLA | Wu, Y. et al. "Label distribution feature selection based on neighborhood rough set" . | Concurrency and Computation: Practice and Experience 36 . 23 (2024) . |
| APA | Wu, Y. , Guo, W. , Lin, Y. . Label distribution feature selection based on neighborhood rough set . | Concurrency and Computation: Practice and Experience , 2024 , 36 (23) . |
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Stereoscopic images typically consist of left and right views along with depth information. Assessing the quality of stereoscopic/3D images (SIQA) is often more complex than that of 2D images due to scene disparities between the left and right views and the intricate process of fusion in binocular vision. To address the problem of quality prediction bias of multi-distortion images, we investigated the visual physiology and the processing of visual information by the primary visual cortex of the human brain and proposed a no-reference stereoscopic image quality evaluation method. The method mainly includes an innovative end-to-end NR-SIQA neural network with a picture patch generation algorithm. The algorithm generates a saliency map by fusing the left and right views and then guides the image cropping in the database based on the saliency map. The proposed models are validated and compared based on publicly available databases. The results show that the model and algorithm together outperform the state-of-the-art NR-SIQA metric in the LIVE 3D database and the WIVC 3D database, and have excellent results in the specific noise metric. The model generalization experiments demonstrate a certain degree of generality of our proposed model. © 2024 Elsevier Ltd
Keyword ：
Image quality Image quality Stereo image processing Stereo image processing
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| GB/T 7714 | Wang, Hanling , Ke, Xiao , Guo, Wenzhong et al. No-reference stereoscopic image quality assessment based on binocular collaboration [J]. | Neural Networks , 2024 , 180 . |
| MLA | Wang, Hanling et al. "No-reference stereoscopic image quality assessment based on binocular collaboration" . | Neural Networks 180 (2024) . |
| APA | Wang, Hanling , Ke, Xiao , Guo, Wenzhong , Zheng, Wukun . No-reference stereoscopic image quality assessment based on binocular collaboration . | Neural Networks , 2024 , 180 . |
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