Query：
学者姓名：张春阳
Refining：
Year
Type
Indexed by
Source
Complex
Co-
Language
Clean All
Abstract ：
Accurate decision-making within highly interactive driving environments is vital for the safety of self-driving vehicles. Despite the significant progress achieved by the existing models for autonomous vehicle decision-making tasks, there remains untapped potential for further exploration in this field. Previous models have focused primarily on specific scenarios or single tasks, with inefficient sample utilization and weak robustness problems, making them challenging to apply in practice. Motivated by this, a robust decision-making method named DRL-EPKG is proposed, which enables the simultaneous determination of vertical and horizontal behaviors of driverless vehicles without being limited to specific driving scenarios. Specifically, the DRL-EPKG integrates human driving knowledge into a framework of soft actor-critic (SAC), where we derive expert policy by a generative model: variational autoencoders (VAE), train agent policy by employing the SAC algorithm and further guide the behaviors of the agent by regulating the Wasserstein distance between the two policies. Moreover, a multidimensional reward function is designed to comprehensively consider safety, driving velocity, energy efficiency, and passenger comfort. Finally, several baseline models are employed for comparative evaluation in three highly dynamic driving scenarios. The findings demonstrate that the proposed model outperforms the baselines regarding the success rate, highlighting the practical applicability and robustness of DRL-EPKG in addressing complex, real-world problems in autonomous driving.
Keyword ：
Autonomous vehicles Autonomous vehicles Decision-making Decision-making Deep reinforcement learning Deep reinforcement learning Human driving knowledge Human driving knowledge
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Li, Feng-Jie , Zhang, Chun-Yang , Chen, C. L. Philip . Robust decision-making for autonomous vehicles via deep reinforcement learning and expert guidance [J]. | APPLIED INTELLIGENCE , 2025 , 55 (6) . |
| MLA | Li, Feng-Jie 等. "Robust decision-making for autonomous vehicles via deep reinforcement learning and expert guidance" . | APPLIED INTELLIGENCE 55 . 6 (2025) . |
| APA | Li, Feng-Jie , Zhang, Chun-Yang , Chen, C. L. Philip . Robust decision-making for autonomous vehicles via deep reinforcement learning and expert guidance . | APPLIED INTELLIGENCE , 2025 , 55 (6) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
With the rise of contrastive learning, unsupervised graph representation learning (GRL) has shown strong competitiveness. However, existing graph contrastive models typically either focus on the local view of graphs or take simple considerations of both global and local views. This may cause these models to overemphasize the importance of individual nodes and their ego networks, or to result in poor learning of global knowledge and affect the learning of local views. Additionally, most GRL models pay attention to topological proximity, assuming that nodes that are closer in graph topology are more similar. However, in the real world, close nodes may be dissimilar, which makes the learned embeddings incorporate inappropriate messages and thus lack discrimination. To address these issues, we propose a novel unsupervised GRL model by contrasting cluster assignments, called graph representation learning model via contrasting cluster assignment (GRCCA). To comprehensively explore the global and local views, it combines multiview contrastive learning and clustering algorithms with an opposite augmentation strategy. It leverages clustering algorithms to capture fine-grained global information and explore potential relevance between nodes in different augmented perspectives while preserving high-quality global and local information through contrast between nodes and prototypes. The opposite augmentation strategy further enhances the contrast of both views, allowing the model to excavate more invariant features. Experimental results show that GRCCA has strong competitiveness compared to state-of-the-art models in different graph analysis tasks.
Keyword ：
Clustering algorithms Clustering algorithms Contrastive learning Contrastive learning graph data mining graph data mining graph representation learning (GRL) graph representation learning (GRL) Prototypes Prototypes Representation learning Representation learning Social networking (online) Social networking (online) Task analysis Task analysis Topology Topology unsupervised learning unsupervised learning Unsupervised learning Unsupervised learning
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhang, Chun-Yang , Yao, Hong-Yu , Chen, C. L. Philip et al. Graph Representation Learning via Contrasting Cluster Assignments [J]. | IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS , 2024 , 16 (3) : 912-922 . |
| MLA | Zhang, Chun-Yang et al. "Graph Representation Learning via Contrasting Cluster Assignments" . | IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 16 . 3 (2024) : 912-922 . |
| APA | Zhang, Chun-Yang , Yao, Hong-Yu , Chen, C. L. Philip , Lin, Yue-Na . Graph Representation Learning via Contrasting Cluster Assignments . | IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS , 2024 , 16 (3) , 912-922 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Graph representation learning focuses on abstracting critical information from raw graphs. Unfortunately, there always exist various kinds of uncertainties, such as attribute noise and network topology corruption, in raw graphs. Under the message passing mechanism, certainties are likely to spread throughout the whole graph. Matters like these would induce deep graph models into producing uncertain representations and restrict representation expressiveness. Considering this, we propose a pioneering framework to defend graph uncertainties by improving the robustness and capability of graph neural networks (GNNs). In our framework, we consider that weights and biases are all fuzzy numbers, thus generating representations to assimilate graph uncertainties, which are finally released by defuzzification. To describe the process of the framework, in this article, a graph convolutional network (GCN) is employed to construct a robust graph model, called FuzzyGCN. To verify the effectiveness of FuzzyGCN, it is trained in both supervised and unsupervised ways. In the supervised setting, we find that FuzzyGCN has stronger power and is more immune to data uncertainties when compared with various classical and robust GNNs. In the unsupervised setting, FuzzyGCN surpasses many state-of-the-art models in node classification and community detection over several real-world datasets.
Keyword ：
Fuzzy graph neural network (FGNN) Fuzzy graph neural network (FGNN) fuzzy graph representation learning (GRL) fuzzy graph representation learning (GRL) fuzzy number fuzzy number graph uncertainty graph uncertainty
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Lin, Yue-Na , Cai, Hai-Chun , Zhang, Chun-Yang et al. Fuzzy Neural Network for Representation Learning on Uncertain Graphs [J]. | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2024 , 32 (9) : 5259-5271 . |
| MLA | Lin, Yue-Na et al. "Fuzzy Neural Network for Representation Learning on Uncertain Graphs" . | IEEE TRANSACTIONS ON FUZZY SYSTEMS 32 . 9 (2024) : 5259-5271 . |
| APA | Lin, Yue-Na , Cai, Hai-Chun , Zhang, Chun-Yang , Yao, Hong-Yu , Philip Chen, C. L. . Fuzzy Neural Network for Representation Learning on Uncertain Graphs . | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2024 , 32 (9) , 5259-5271 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Graph representation learning has recently garnered significant attention due to its wide applications in graph analysis tasks. It is well-known that real -world networks are dynamic, with edges and nodes evolving over time. This presents unique challenges that are distinct from those of static networks. However, most graph representation learning methods are either designed for static graphs, or address only partial challenges associated with dynamic graphs. They overlook the intricate interplay between topology and temporality in the evolution of dynamic graphs and the complexity of sequence modeling. Therefore, we propose a new dynamic graph representation learning model, called as R-GraphSAGE, which takes comprehensive considerations for embedding dynamic graphs. By incorporating a recurrent structure into GraphSAGE, the proposed RGraphSAGE explores structural and temporal patterns integrally to capture more fine-grained evolving patterns of dynamic graphs. Additionally, it offers a lightweight architecture to decrease the computational costs for handling snapshot sequences, achieving a balance between performance and complexity. Moreover, it can inductively process the addition of new nodes and adapt to the situations without labels and node attributes. The performance of the proposed R-GraphSAGE is evaluated across various downstream tasks with both synthetic and real -world networks. The experimental results demonstrate that it outperforms state-of-the-art baselines by a significant margin in most cases.
Keyword ：
Dynamic networks Dynamic networks Graph representation learning Graph representation learning Inductive learning Inductive learning Recurrent graph neural networks Recurrent graph neural networks Unsupervised learning Unsupervised learning
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Yao, Hong-Yu , Zhang, Chun-Yang , Yao, Zhi-Liang et al. A recurrent graph neural network for inductive representation learning on dynamic graphs [J]. | PATTERN RECOGNITION , 2024 , 154 . |
| MLA | Yao, Hong-Yu et al. "A recurrent graph neural network for inductive representation learning on dynamic graphs" . | PATTERN RECOGNITION 154 (2024) . |
| APA | Yao, Hong-Yu , Zhang, Chun-Yang , Yao, Zhi-Liang , Chen, C. L. Philip , Hu, Junfeng . A recurrent graph neural network for inductive representation learning on dynamic graphs . | PATTERN RECOGNITION , 2024 , 154 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Existing works have made some progress in point cloud registration, but most of them measure performance only on point cloud pairs with high overlap. In practical applications, it is often difficult to ensure that the collected point clouds overlap in large regions due to problems such as occlusion and noise. Therefore, a good low-overlap point cloud registration method is of great practical significance. However, extracting reliable correspondences from point clouds has always been a challenging task, particularly when dealing with low-overlap situation. In this paper, we propose a novel method for low-overlap point cloud registration via efficient correspondence augmentation, called AugLPCR, which not only enhances correspondences with high confidence, but also employs confidence weights to mitigate the impact of outliers. After the augmentation, the correspondences used for the transformation have a large amount of inliers, leading to improved registration performance. Extensive experiments on indoor and outdoor datasets demonstrate that the proposed AugLPCR is capable of maintaining consistent performance and achieve results comparable to or better than the state-of-the-art methods. Note to Practitioners-The motivation of this paper is to address the problem of registering two low-overlap point clouds. Mainstream algorithms for point cloud registration typically assume a sufficient overlap between point clouds. However, in practical scenarios, it is common to encounter scans with inadequate overlap. These conditions often hinder the extraction of reliable correspondences. This paper introduces an effective method for augmenting correspondences to address the problem of low inlier rates within predicted correspondences. While augmenting correspondences with high confidence, it also mitigates the influence of outliers and ambiguous points. Additionally, traditional approaches often divide superpoint regions before matching, but this can lead to the elimination of points in overlapping regions alongside outliers. To address this issue, we adjust the order of superpoint matching and region partitioning. The proposed framework can be easily applied to other correspondence-based point cloud registration models.
Keyword ：
Accuracy Accuracy Convolution Convolution correspondence augmentation correspondence augmentation Estimation Estimation Feature extraction Feature extraction Image color analysis Image color analysis Iterative methods Iterative methods Point cloud compression Point cloud compression Point cloud registration Point cloud registration point cloud visualization point cloud visualization Reflection Reflection Three-dimensional displays Three-dimensional displays Transforms Transforms
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Lin, Zhi-Huang , Zhang, Chun-Yang , Lin, Xue-Ming et al. Low-Overlap Point Cloud Registration via Correspondence Augmentation [J]. | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2024 , 22 : 9363-9375 . |
| MLA | Lin, Zhi-Huang et al. "Low-Overlap Point Cloud Registration via Correspondence Augmentation" . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 22 (2024) : 9363-9375 . |
| APA | Lin, Zhi-Huang , Zhang, Chun-Yang , Lin, Xue-Ming , Lin, Huibin , Zeng, Gui-Huang , Chen, C. L. Philip . Low-Overlap Point Cloud Registration via Correspondence Augmentation . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2024 , 22 , 9363-9375 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
The goal of multi-document summarization (MDS) is to generate a comprehensive and concise summary from multiple documents, which should not only be grammatically correct but also semantically contains the refined content of the overall texts. Existing summarizers based on sequential pre-trained large language models often cognize documents as linear sequences, which overlook the hierarchical structure correlations of sentences and paragraphs within or between documents. Additionally, those models also have limitations in handling long text input. To alleviate these two problems, a multi-document summarization model is proposed, with a heterogeneous graph of sentences, paragraphs and documents, called HeterMDS, to uncover deep semantic meanings and local-global context within documents. By integrating large language model and graph encoder with bootstrapped graph latents, the proposed HeterMDS can learn a semantically rich document representation and generate a coherent, concise and fact-consistent summary. It can be flexibly applied to current pre-trained language models, effectively improving their performance in MDS. Extensive experiment results can verify the effectiveness of the proposed HeterMDS and its contained modules, and demonstrate its competitiveness against the state-of-the-art models. © 2016 IEEE.
Keyword ：
bootstrapped graph latents bootstrapped graph latents graph representation learning graph representation learning heterogeneous graph heterogeneous graph large language models large language models Multi-document summarization Multi-document summarization
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zeng, G.-H. , Liu, Y.-Q. , Zhang, C.-Y. et al. Adaptive Multi-Document Summarization Via Graph Representation Learning [J]. | IEEE Transactions on Cognitive and Developmental Systems , 2024 . |
| MLA | Zeng, G.-H. et al. "Adaptive Multi-Document Summarization Via Graph Representation Learning" . | IEEE Transactions on Cognitive and Developmental Systems (2024) . |
| APA | Zeng, G.-H. , Liu, Y.-Q. , Zhang, C.-Y. , Cai, H.-C. , Chen, C.L.P. . Adaptive Multi-Document Summarization Via Graph Representation Learning . | IEEE Transactions on Cognitive and Developmental Systems , 2024 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Unsupervised person re-identification (Re-ID) is more substantial than the supervised one because it does not require any labeled samples. Currently, the most advanced unsupervised Re-ID models generate pseudo-labels to group images into different clusters and then establish a memory bank to calculate contrastive loss between instances and clusters. This framework has been proven to be remarkably efficient for unsupervised person Re-ID tasks. However, clustering operation inevitably produces misclassification, which brings noises and difficulties to contrastive learning and affects the initialization and updating of the prototype features stored in the memory bank. To solve this problem, we propose a new robust unsupervised person Re-ID model with two developed modules: Cluster Sample Aggregation module (CSA) and Hard Positive Sampling strategy (HPS). The CSA module aggregates each sample in the same cluster through the multi-head self-attention mechanism. This process enables the initialization of prototypes based on the similarities observed within clusters. Additionally, the HPS strategy extracts the dispersion degree of each sample by means of a self-attention aggregation module (SAA) that has been trained by CSA module. According to the obtained indicators, the hardest positive sample is sampled to update the prototype feature stored in the memory bank. With the self-attention mechanism fusing the information among instances in each cluster, the implicit relationships between samples can be better explored in a more refined way. Experiments show that our method achieves state-of-the-art results against existing unsupervised baselines on Market-1501, PersonX, and MSMT17 datasets.
Keyword ：
Contrastive learning Contrastive learning Person Re-ID Person Re-ID Self-attention Self-attention Unsupervised learning Unsupervised learning
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Lin, Huibin , Fu, Hai-Tao , Zhang, Chun-Yang et al. A new robust contrastive learning for unsupervised person re-identification [J]. | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS , 2023 , 15 (5) : 1779-1793 . |
| MLA | Lin, Huibin et al. "A new robust contrastive learning for unsupervised person re-identification" . | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 15 . 5 (2023) : 1779-1793 . |
| APA | Lin, Huibin , Fu, Hai-Tao , Zhang, Chun-Yang , Chen, C. L. Philip . A new robust contrastive learning for unsupervised person re-identification . | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS , 2023 , 15 (5) , 1779-1793 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Semi-supervised learning is a common way that investigates how to improve performance of a visual learning model, while data annotation is far from sufficient. Recent works in semi-supervised deep learning have successfully applied consistency regularization, which encourages a model to maintain consistent predictions for different perturbed versions of an image. However, most of such methods ignore the category correlation of image features, especially when exploiting strong augmentation methods for unlabeled images. To address this problem, we propose PConMatch, a model that leverages a probabilistic contrastive learning framework to separate the features of strongly-augmented versions from different classes. A semi-supervised probabilistic contrastive loss is designed, which takes both labeled and unlabeled samples into account and develops an auxiliary module to generate a probability score to measure the model prediction confidence for each sample. Specifically, PConMatch first generates a pair of weakly-augmented versions for each labeled sample, and produces a weakly-augmented version and a corresponding pair of strongly-augmented versions for each unlabeled sample. Second, a probability score module is proposed to assign pseudo-labeling confidence scores to strongly-augmented unlabeled images. Finally, the probability score of each sample is further passed to the contrastive loss, combining with consistency regularization to enable the model to learn better feature representations. Extensive experiments on four publicly available image classification benchmarks demonstrate that the proposed approach achieves state-of-the-art performance in image classification. Several rigorous ablation studies are conducted to validate the effectiveness of the method.
Keyword ：
Annotations Annotations consistency regularization consistency regularization contrastive learning contrastive learning data augmen- tation data augmen- tation image classification image classification Image classification Image classification Predictive models Predictive models Probabilistic logic Probabilistic logic Pseudo-labeling Pseudo-labeling Semantics Semantics Semi-supervised learning Semi-supervised learning Task analysis Task analysis Visualization Visualization
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Lin, Huibin , Zhang, Chun-Yang , Wang, Shiping et al. A Probabilistic Contrastive Framework for Semi-Supervised Learning [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2023 , 25 : 8767-8779 . |
| MLA | Lin, Huibin et al. "A Probabilistic Contrastive Framework for Semi-Supervised Learning" . | IEEE TRANSACTIONS ON MULTIMEDIA 25 (2023) : 8767-8779 . |
| APA | Lin, Huibin , Zhang, Chun-Yang , Wang, Shiping , Guo, Wenzhong . A Probabilistic Contrastive Framework for Semi-Supervised Learning . | IEEE TRANSACTIONS ON MULTIMEDIA , 2023 , 25 , 8767-8779 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Recent years have witnessed a drastic surge in graph representation learning, which usually produces low-dimensional and crisp representations from graph topology and high-dimensional node attributes. Nevertheless, a crisp representation of a node or graph actually conceals the uncertainty and interpretability of features. In citation networks, for example, the reference between the two papers is always involved with fuzziness denoting the correlation degrees, that is, one connection may simultaneously belong to strong and weak references in different beliefs. The uncertainty in node connections and attributes inspires us to delve into fuzzy representations. This article, for the first time, proposes an unsupervised fuzzy representation learning model for graphs and networks to improve their expressiveness by making crisp representations fuzzy. Specifically, we develop a fuzzy graph convolution neural network (FGCNN), which could aggregate high-level fuzzy features, leveraging fuzzy logic to fully excavate feature-level uncertainties, and finally generate fuzzy representations. The corresponding hierarchical model composed of multiple FGCNNs, called deep fuzzy graph convolution neural network (DFGCNN), is able to generate fuzzy node representations which are more expressive than crisp ones. Experimental results of multiple network analysis tasks validate that the proposed fuzzy representation models have strong competitiveness against the state-of-the-art baselines over several real-world datasets.
Keyword ：
Fuzzy logic Fuzzy logic fuzzy representation fuzzy representation graph convolutional network graph convolutional network graph representation graph representation
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhang, Chun-Yang , Lin, Yue-Na , Chen, C. L. Philip et al. Fuzzy Representation Learning on Graph [J]. | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2023 , 31 (10) : 3358-3370 . |
| MLA | Zhang, Chun-Yang et al. "Fuzzy Representation Learning on Graph" . | IEEE TRANSACTIONS ON FUZZY SYSTEMS 31 . 10 (2023) : 3358-3370 . |
| APA | Zhang, Chun-Yang , Lin, Yue-Na , Chen, C. L. Philip , Yao, Hong-Yu , Cai, Hai-Chun , Fang, Wu-Peng . Fuzzy Representation Learning on Graph . | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2023 , 31 (10) , 3358-3370 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Accurate prediction of vehicle trajectories is crucial to the safety and comfort of autonomous vehicles. Although several graph-based models have exhibited substantial progress in acquiring spatiotemporal dependencies among vehicles in the driving environment, the potential for additional exploration in this domain persists. The main reason is that they concentrated on independently capturing the spatial relations and temporal dependencies, neglecting to incorporate the temporal feature into the spatial feature for co-training, which limits their ability to yield satisfactory predictive accuracy. Typically, spatial and temporal correlations are coupled and should be modeled jointly. Inspired by this, a novel dynamic graph neural network with spatial-temporal synchronization (STS-DGNN) for vehicle trajectory prediction is proposed, which constructs the driving scene as dynamic graphs and can jointly extract spatial-temporal features. Specifically, low-order and high-order dynamics of vehicle trajectories are considered collaboratively in a one-stage framework rather than independently modeling the spatial relationship and temporal correlations of vehicles in two-stage models. The proposed model also considers the dynamic nature of graph sequence by utilizing gate recurrent unit (GRU) to update the graph neural network (GNN) parameters dynamically. The spatial-temporal features are subsequently conveyed to convolutional neural networks (CNNs) and processed by a multilayer perceptron (MLP) to generate the ultimate trajectories. Finally, to illustrate the effectiveness of the STSDGNN model, the model is assessed on three well-known datasets, namely highD, EWAP, and UCY. The results confirm that our model performs better at making predictions than cuttingedge models. The visualization results intuitively explain that our method can extract sophisticated and subtle multivehicle interactions, resulting in accurate predictions.
Keyword ：
~Autonomous driving ~Autonomous driving dynamic graph dynamic graph graph neural network (GNN) graph neural network (GNN) spatial-temporal dependencies spatial-temporal dependencies vehicle trajectory prediction vehicle trajectory prediction
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Li, Feng-Jie , Zhang, Chun-Yang , Chen, C. L. Philip . STS-DGNN: Vehicle Trajectory Prediction via Dynamic Graph Neural Network With Spatial-Temporal Synchronization [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2023 , 72 . |
| MLA | Li, Feng-Jie et al. "STS-DGNN: Vehicle Trajectory Prediction via Dynamic Graph Neural Network With Spatial-Temporal Synchronization" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 72 (2023) . |
| APA | Li, Feng-Jie , Zhang, Chun-Yang , Chen, C. L. Philip . STS-DGNN: Vehicle Trajectory Prediction via Dynamic Graph Neural Network With Spatial-Temporal Synchronization . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2023 , 72 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
