Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating In...Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.展开更多
The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats.Intrusion detection systems are cr...The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats.Intrusion detection systems are crucial to network security,playing a pivotal role in safeguarding networks from potential threats.However,in the context of an evolving landscape of sophisticated and elusive attacks,existing intrusion detection methodologies often overlook critical aspects such as changes in network topology over time and interactions between hosts.To address these issues,this paper proposes a real-time network intrusion detection method based on graph neural networks.The proposedmethod leverages the advantages of graph neural networks and employs a straightforward graph construction method to represent network traffic as dynamic graph-structured data.Additionally,a graph convolution operation with a multi-head attention mechanism is utilized to enhance the model’s ability to capture the intricate relationships within the graph structure comprehensively.Furthermore,it uses an integrated graph neural network to address dynamic graphs’structural and topological changes at different time points and the challenges of edge embedding in intrusion detection data.The edge classification problem is effectively transformed into node classification by employing a line graph data representation,which facilitates fine-grained intrusion detection tasks on dynamic graph node feature representations.The efficacy of the proposed method is evaluated using two commonly used intrusion detection datasets,UNSW-NB15 and NF-ToN-IoT-v2,and results are compared with previous studies in this field.The experimental results demonstrate that our proposed method achieves 99.3%and 99.96%accuracy on the two datasets,respectively,and outperforms the benchmark model in several evaluation metrics.展开更多
Hyperspectral image(HSI)classification is crucial for numerous remote sensing applications.Traditional deep learning methods may miss pixel relationships and context,leading to inefficiencies.This paper introduces the...Hyperspectral image(HSI)classification is crucial for numerous remote sensing applications.Traditional deep learning methods may miss pixel relationships and context,leading to inefficiencies.This paper introduces the spectral band graph convolutional and attention-enhanced CNN joint network(SGCCN),a novel approach that harnesses the power of spectral band graph convolutions for capturing long-range relationships,utilizes local perception of attention-enhanced multi-level convolutions for local spatial feature and employs a dynamic attention mechanism to enhance feature extraction.The SGCCN integrates spectral and spatial features through a self-attention fusion network,significantly improving classification accuracy and efficiency.The proposed method outperforms existing techniques,demonstrating its effectiveness in handling the challenges associated with HSI data.展开更多
Automatically extracting Drug-Drug Interactions (DDIs) from text is a crucial and challenging task, particularly when multiple medications are taken concurrently. In this study, we propose a novel approach, called Enh...Automatically extracting Drug-Drug Interactions (DDIs) from text is a crucial and challenging task, particularly when multiple medications are taken concurrently. In this study, we propose a novel approach, called Enhanced Attention-driven Dynamic Graph Convolutional Network (E-ADGCN), for DDI extraction. Our model combines the Attention-driven Dynamic Graph Convolutional Network (ADGCN) with a feature fusion method and multi-task learning framework. The ADGCN effectively utilizes entity information and dependency tree information from biomedical texts to extract DDIs. The feature fusion method integrates User-Generated Content (UGC) and molecular information with drug entity information from text through dynamic routing. By leveraging external resources, our approach maximizes the auxiliary effect and improves the accuracy of DDI extraction. We evaluate the E-ADGCN model on the extended DDIExtraction2013 dataset and achieve an F1-score of 81.45%. This research contributes to the advancement of automated methods for extracting valuable drug interaction information from textual sources, facilitating improved medication management and patient safety.展开更多
The analysis of interwell connectivity plays an important role in the formulation of oilfield development plans and the description of residual oil distribution. In fact, sandstone reservoirs in China's onshore oi...The analysis of interwell connectivity plays an important role in the formulation of oilfield development plans and the description of residual oil distribution. In fact, sandstone reservoirs in China's onshore oilfields generally have the characteristics of thin and many layers, so multi-layer joint production is usually adopted. It remains a challenge to ensure the accuracy of splitting and dynamic connectivity in each layer of the injection-production wells with limited field data. The three-dimensional well pattern of multi-layer reservoir and the relationship between injection-production wells can be equivalent to a directional heterogeneous graph. In this paper, an improved graph neural network is proposed to construct an interacting process mimics the real interwell flow regularity. In detail, this method is used to split injection and production rates by combining permeability, porosity and effective thickness, and to invert the dynamic connectivity in each layer of the injection-production wells by attention mechanism.Based on the material balance and physical information, the overall connectivity from the injection wells,through the water injection layers to the production layers and the output of final production wells is established. Meanwhile, the change of well pattern caused by perforation, plugging and switching of wells at different times is achieved by updated graph structure in spatial and temporal ways. The effectiveness of the method is verified by a combination of reservoir numerical simulation examples and field example. The method corresponds to the actual situation of the reservoir, has wide adaptability and low cost, has good practical value, and provides a reference for adjusting the injection-production relationship of the reservoir and the development of the remaining oil.展开更多
Accurately predicting the State of Health(SOH)of lithium-ion batteries is a critical challenge to ensure their reliability and safety in energy storage systems,such as electric vehicles and renewable energy grids.The ...Accurately predicting the State of Health(SOH)of lithium-ion batteries is a critical challenge to ensure their reliability and safety in energy storage systems,such as electric vehicles and renewable energy grids.The intricate battery degradation process is influenced by evolving spatial and temporal interactions among health indicators.Existing methods often fail to capture the dynamic interactions between health indicators over time,resulting in limited predictive accuracy.To address these challenges,we propose a novel framework,Dynamic Graph Learning with Spatial-Temporal Fusion Attention(DGL-STFA),which transforms health indicator series time-data into time-evolving graph representations.The framework employs multi-scale convolutional neural networks to capture diverse temporal patterns,a self-attention mechanism to construct dynamic adjacency matrices that adapt over time,and a temporal attention mechanism to identify and prioritize key moments that influence battery degradation.This combination enables DGL-STFA to effectively model both dynamic spatial relationships and long-term temporal dependencies,enhancing SOH prediction accuracy.Extensive experiments were conducted on the NASA and CALCE battery datasets,comparing this framework with traditional time-series prediction methods and other graph-based prediction methods.The results demonstrate that our framework significantly improves prediction accuracy,with a mean absolute error more than 30%lower than other methods.Further analysis demonstrated the robustness of DGL-STFA across various battery life stages,including early,mid,and end-of-life phases.These results highlight the capability of DGL-STFA to accurately predict SOH,addressing critical challenges in advancing battery health monitoring for energy storage applications.展开更多
Deep neural network-based relational extraction research has made significant progress in recent years,andit provides data support for many natural language processing downstream tasks such as building knowledgegraph,...Deep neural network-based relational extraction research has made significant progress in recent years,andit provides data support for many natural language processing downstream tasks such as building knowledgegraph,sentiment analysis and question-answering systems.However,previous studies ignored much unusedstructural information in sentences that could enhance the performance of the relation extraction task.Moreover,most existing dependency-based models utilize self-attention to distinguish the importance of context,whichhardly deals withmultiple-structure information.To efficiently leverage multiple structure information,this paperproposes a dynamic structure attention mechanism model based on textual structure information,which deeplyintegrates word embedding,named entity recognition labels,part of speech,dependency tree and dependency typeinto a graph convolutional network.Specifically,our model extracts text features of different structures from theinput sentence.Textual Structure information Graph Convolutional Networks employs the dynamic structureattention mechanism to learn multi-structure attention,effectively distinguishing important contextual features invarious structural information.In addition,multi-structure weights are carefully designed as amergingmechanismin the different structure attention to dynamically adjust the final attention.This paper combines these featuresand trains a graph convolutional network for relation extraction.We experiment on supervised relation extractiondatasets including SemEval 2010 Task 8,TACRED,TACREV,and Re-TACED,the result significantly outperformsthe previous.展开更多
Human-object interaction(HOIs)detection is a new branch of visual relationship detection,which plays an important role in the field of image understanding.Because of the complexity and diversity of image content,the d...Human-object interaction(HOIs)detection is a new branch of visual relationship detection,which plays an important role in the field of image understanding.Because of the complexity and diversity of image content,the detection of HOIs is still an onerous challenge.Unlike most of the current works for HOIs detection which only rely on the pairwise information of a human and an object,we propose a graph-based HOIs detection method that models context and global structure information.Firstly,to better utilize the relations between humans and objects,the detected humans and objects are regarded as nodes to construct a fully connected undirected graph,and the graph is pruned to obtain an HOI graph that only preserving the edges connecting human and object nodes.Then,in order to obtain more robust features of human and object nodes,two different attention-based feature extraction networks are proposed,which model global and local contexts respectively.Finally,the graph attention network is introduced to pass messages between different nodes in the HOI graph iteratively,and detect the potential HOIs.Experiments on V-COCO and HICO-DET datasets verify the effectiveness of the proposed method,and show that it is superior to many existing methods.展开更多
This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary obj...This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary objective is to explore the unknown environments to locate and track targets effectively. To address this problem, we propose a novel Multi-Agent Reinforcement Learning (MARL) method based on Graph Neural Network (GNN). Firstly, a method is introduced for encoding continuous-space multi-UAV problem data into spatial graphs which establish essential relationships among agents, obstacles, and targets. Secondly, a Graph AttenTion network (GAT) model is presented, which focuses exclusively on adjacent nodes, learns attention weights adaptively and allows agents to better process information in dynamic environments. Reward functions are specifically designed to tackle exploration challenges in environments with sparse rewards. By introducing a framework that integrates centralized training and distributed execution, the advancement of models is facilitated. Simulation results show that the proposed method outperforms the existing MARL method in search rate and tracking performance with less collisions. The experiments show that the proposed method can be extended to applications with a larger number of agents, which provides a potential solution to the challenging problem of multi-UAV autonomous tracking in dynamic unknown environments.展开更多
Graph convolutional networks(GCNs)have become a dominant approach for skeleton-based action recognition tasks.Although GCNs have made significant progress in modeling skeletons as spatial-temporal graphs,they often re...Graph convolutional networks(GCNs)have become a dominant approach for skeleton-based action recognition tasks.Although GCNs have made significant progress in modeling skeletons as spatial-temporal graphs,they often require stacking multiple graph convolution layers to effectively capture long-distance relationships among nodes.This stacking not only increases computational burdens but also raises the risk of over-smoothing,which can lead to the neglect of crucial local action features.To address this issue,we propose a novel multi-scale adaptive large kernel graph convolutional network(MSLK-GCN)to effectively aggregate local and global spatio-temporal correlations while maintaining the computational efficiency.The core components of the network include two multi-scale large kernel graph convolution(LKGC)modules,a multi-channel adaptive graph convolution(MAGC)module,and a multi-scale temporal self-attention convolution(MSTC)module.The LKGC module adaptively focuses on active motion regions by utilizing a large convolution kernel and a gating mechanism,effectively capturing long-distance dependencies within the skeleton sequence.Meanwhile,the MAGC module dynamically learns relationships between different joints by adjusting connection weights between nodes.To further enhance the ability to capture temporal dynamics,the MSTC module effectively aggregates the temporal information by integrating Efficient Channel Attention(ECA)with multi-scale convolution.In addition,we use a multi-stream fusion strategy to make full use of different modal skeleton data,including bone,joint,joint motion,and bone motion.Exhaustive experiments on three scale-varying datasets,i.e.,NTU-60,NTU-120,and NW-UCLA,demonstrate that our MSLK-GCN can achieve state-of-the-art performance with fewer parameters.展开更多
从单张RGB图像中实现双手的3D交互式网格重建是一项极具挑战性的任务。由于双手之间的相互遮挡以及局部外观相似性较高,导致部分特征提取不够准确,从而丢失了双手之间的交互信息并使重建的手部网格与输入图像出现不对齐等问题。为了解...从单张RGB图像中实现双手的3D交互式网格重建是一项极具挑战性的任务。由于双手之间的相互遮挡以及局部外观相似性较高,导致部分特征提取不够准确,从而丢失了双手之间的交互信息并使重建的手部网格与输入图像出现不对齐等问题。为了解决上述问题,本文首先提出一种包含两个部分的特征交互适应模块,第一部分特征交互在保留左右手分离特征的同时生成两种新的特征表示,并通过交互注意力模块捕获双手的交互特征;第二部分特征适应则是将此交互特征利用交互注意力模块适应到每只手,为左右手特征注入全局上下文信息。其次,引入三层图卷积细化网络结构用于精确回归双手网格顶点,并通过基于注意力机制的特征对齐模块增强顶点特征和图像特征的对齐,从而增强重建的手部网格和输入图像的对齐。同时提出一种新的多层感知机结构,通过下采样和上采样操作学习多尺度特征信息。最后,设计相对偏移损失函数约束双手的空间关系。在InterHand2.6M数据集上的定量和定性实验表明,与现有的优秀方法相比,所提出的方法显著提升了模型性能,其中平均每关节位置误差(Mean Per Joint Position Error,MPJPE)和平均每顶点位置误差(Mean Per Vertex Position Error,MPVPE)分别降低至7.19 mm和7.33 mm。此外,在RGB2Hands和EgoHands数据集上进行泛化性实验,定性实验结果表明所提出的方法具有良好的泛化能力,能够适应不同环境背景下的手部网格重建。展开更多
文摘Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.
文摘The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats.Intrusion detection systems are crucial to network security,playing a pivotal role in safeguarding networks from potential threats.However,in the context of an evolving landscape of sophisticated and elusive attacks,existing intrusion detection methodologies often overlook critical aspects such as changes in network topology over time and interactions between hosts.To address these issues,this paper proposes a real-time network intrusion detection method based on graph neural networks.The proposedmethod leverages the advantages of graph neural networks and employs a straightforward graph construction method to represent network traffic as dynamic graph-structured data.Additionally,a graph convolution operation with a multi-head attention mechanism is utilized to enhance the model’s ability to capture the intricate relationships within the graph structure comprehensively.Furthermore,it uses an integrated graph neural network to address dynamic graphs’structural and topological changes at different time points and the challenges of edge embedding in intrusion detection data.The edge classification problem is effectively transformed into node classification by employing a line graph data representation,which facilitates fine-grained intrusion detection tasks on dynamic graph node feature representations.The efficacy of the proposed method is evaluated using two commonly used intrusion detection datasets,UNSW-NB15 and NF-ToN-IoT-v2,and results are compared with previous studies in this field.The experimental results demonstrate that our proposed method achieves 99.3%and 99.96%accuracy on the two datasets,respectively,and outperforms the benchmark model in several evaluation metrics.
基金supported in part by the National Natural Science Foundations of China(No.61801214)the Postgraduate Research Practice Innovation Program of NUAA(No.xcxjh20231504)。
文摘Hyperspectral image(HSI)classification is crucial for numerous remote sensing applications.Traditional deep learning methods may miss pixel relationships and context,leading to inefficiencies.This paper introduces the spectral band graph convolutional and attention-enhanced CNN joint network(SGCCN),a novel approach that harnesses the power of spectral band graph convolutions for capturing long-range relationships,utilizes local perception of attention-enhanced multi-level convolutions for local spatial feature and employs a dynamic attention mechanism to enhance feature extraction.The SGCCN integrates spectral and spatial features through a self-attention fusion network,significantly improving classification accuracy and efficiency.The proposed method outperforms existing techniques,demonstrating its effectiveness in handling the challenges associated with HSI data.
基金supported by the National Natural Science Foundation of China(No.62476025)the Shaanxi Provincial Department of Science and Technology Projects(No.2013K06-39).
文摘Automatically extracting Drug-Drug Interactions (DDIs) from text is a crucial and challenging task, particularly when multiple medications are taken concurrently. In this study, we propose a novel approach, called Enhanced Attention-driven Dynamic Graph Convolutional Network (E-ADGCN), for DDI extraction. Our model combines the Attention-driven Dynamic Graph Convolutional Network (ADGCN) with a feature fusion method and multi-task learning framework. The ADGCN effectively utilizes entity information and dependency tree information from biomedical texts to extract DDIs. The feature fusion method integrates User-Generated Content (UGC) and molecular information with drug entity information from text through dynamic routing. By leveraging external resources, our approach maximizes the auxiliary effect and improves the accuracy of DDI extraction. We evaluate the E-ADGCN model on the extended DDIExtraction2013 dataset and achieve an F1-score of 81.45%. This research contributes to the advancement of automated methods for extracting valuable drug interaction information from textual sources, facilitating improved medication management and patient safety.
基金the support of the National Nature Science Foundation of China(No.52074336)Emerging Big Data Projects of Sinopec Corporation(No.20210918084304712)。
文摘The analysis of interwell connectivity plays an important role in the formulation of oilfield development plans and the description of residual oil distribution. In fact, sandstone reservoirs in China's onshore oilfields generally have the characteristics of thin and many layers, so multi-layer joint production is usually adopted. It remains a challenge to ensure the accuracy of splitting and dynamic connectivity in each layer of the injection-production wells with limited field data. The three-dimensional well pattern of multi-layer reservoir and the relationship between injection-production wells can be equivalent to a directional heterogeneous graph. In this paper, an improved graph neural network is proposed to construct an interacting process mimics the real interwell flow regularity. In detail, this method is used to split injection and production rates by combining permeability, porosity and effective thickness, and to invert the dynamic connectivity in each layer of the injection-production wells by attention mechanism.Based on the material balance and physical information, the overall connectivity from the injection wells,through the water injection layers to the production layers and the output of final production wells is established. Meanwhile, the change of well pattern caused by perforation, plugging and switching of wells at different times is achieved by updated graph structure in spatial and temporal ways. The effectiveness of the method is verified by a combination of reservoir numerical simulation examples and field example. The method corresponds to the actual situation of the reservoir, has wide adaptability and low cost, has good practical value, and provides a reference for adjusting the injection-production relationship of the reservoir and the development of the remaining oil.
基金sponsored by the National Key Research and Development Program of China(No.2023YFB4606200)Key Program of Science and Technology of Yunnan Province,China (No.202302AB080020)Key Project of Shanghai Zhangjiang National Independent hnovation Demonstration Zone,China(No.ZJ2021-2D-006).
文摘Accurately predicting the State of Health(SOH)of lithium-ion batteries is a critical challenge to ensure their reliability and safety in energy storage systems,such as electric vehicles and renewable energy grids.The intricate battery degradation process is influenced by evolving spatial and temporal interactions among health indicators.Existing methods often fail to capture the dynamic interactions between health indicators over time,resulting in limited predictive accuracy.To address these challenges,we propose a novel framework,Dynamic Graph Learning with Spatial-Temporal Fusion Attention(DGL-STFA),which transforms health indicator series time-data into time-evolving graph representations.The framework employs multi-scale convolutional neural networks to capture diverse temporal patterns,a self-attention mechanism to construct dynamic adjacency matrices that adapt over time,and a temporal attention mechanism to identify and prioritize key moments that influence battery degradation.This combination enables DGL-STFA to effectively model both dynamic spatial relationships and long-term temporal dependencies,enhancing SOH prediction accuracy.Extensive experiments were conducted on the NASA and CALCE battery datasets,comparing this framework with traditional time-series prediction methods and other graph-based prediction methods.The results demonstrate that our framework significantly improves prediction accuracy,with a mean absolute error more than 30%lower than other methods.Further analysis demonstrated the robustness of DGL-STFA across various battery life stages,including early,mid,and end-of-life phases.These results highlight the capability of DGL-STFA to accurately predict SOH,addressing critical challenges in advancing battery health monitoring for energy storage applications.
文摘Deep neural network-based relational extraction research has made significant progress in recent years,andit provides data support for many natural language processing downstream tasks such as building knowledgegraph,sentiment analysis and question-answering systems.However,previous studies ignored much unusedstructural information in sentences that could enhance the performance of the relation extraction task.Moreover,most existing dependency-based models utilize self-attention to distinguish the importance of context,whichhardly deals withmultiple-structure information.To efficiently leverage multiple structure information,this paperproposes a dynamic structure attention mechanism model based on textual structure information,which deeplyintegrates word embedding,named entity recognition labels,part of speech,dependency tree and dependency typeinto a graph convolutional network.Specifically,our model extracts text features of different structures from theinput sentence.Textual Structure information Graph Convolutional Networks employs the dynamic structureattention mechanism to learn multi-structure attention,effectively distinguishing important contextual features invarious structural information.In addition,multi-structure weights are carefully designed as amergingmechanismin the different structure attention to dynamically adjust the final attention.This paper combines these featuresand trains a graph convolutional network for relation extraction.We experiment on supervised relation extractiondatasets including SemEval 2010 Task 8,TACRED,TACREV,and Re-TACED,the result significantly outperformsthe previous.
基金Project(51678075)supported by the National Natural Science Foundation of ChinaProject(2017GK2271)supported by the Hunan Provincial Science and Technology Department,China。
文摘Human-object interaction(HOIs)detection is a new branch of visual relationship detection,which plays an important role in the field of image understanding.Because of the complexity and diversity of image content,the detection of HOIs is still an onerous challenge.Unlike most of the current works for HOIs detection which only rely on the pairwise information of a human and an object,we propose a graph-based HOIs detection method that models context and global structure information.Firstly,to better utilize the relations between humans and objects,the detected humans and objects are regarded as nodes to construct a fully connected undirected graph,and the graph is pruned to obtain an HOI graph that only preserving the edges connecting human and object nodes.Then,in order to obtain more robust features of human and object nodes,two different attention-based feature extraction networks are proposed,which model global and local contexts respectively.Finally,the graph attention network is introduced to pass messages between different nodes in the HOI graph iteratively,and detect the potential HOIs.Experiments on V-COCO and HICO-DET datasets verify the effectiveness of the proposed method,and show that it is superior to many existing methods.
基金supported by the National Natural Science Foundation of China(Nos.12272104,U22B2013).
文摘This paper investigates the challenges associated with Unmanned Aerial Vehicle (UAV) collaborative search and target tracking in dynamic and unknown environments characterized by limited field of view. The primary objective is to explore the unknown environments to locate and track targets effectively. To address this problem, we propose a novel Multi-Agent Reinforcement Learning (MARL) method based on Graph Neural Network (GNN). Firstly, a method is introduced for encoding continuous-space multi-UAV problem data into spatial graphs which establish essential relationships among agents, obstacles, and targets. Secondly, a Graph AttenTion network (GAT) model is presented, which focuses exclusively on adjacent nodes, learns attention weights adaptively and allows agents to better process information in dynamic environments. Reward functions are specifically designed to tackle exploration challenges in environments with sparse rewards. By introducing a framework that integrates centralized training and distributed execution, the advancement of models is facilitated. Simulation results show that the proposed method outperforms the existing MARL method in search rate and tracking performance with less collisions. The experiments show that the proposed method can be extended to applications with a larger number of agents, which provides a potential solution to the challenging problem of multi-UAV autonomous tracking in dynamic unknown environments.
基金supported in part by the National Natural Science Foundation of China under Grant No.61976127the Shandong Provincial Natural Science Foundation under Grant No.ZR2024MF030+1 种基金the Taishan Scholar Program of Shandong Province of China under Grant No.tsqn202306150the Key Research and Development Program of Shandong Province of China under Grant No.2025CXPT096.
文摘Graph convolutional networks(GCNs)have become a dominant approach for skeleton-based action recognition tasks.Although GCNs have made significant progress in modeling skeletons as spatial-temporal graphs,they often require stacking multiple graph convolution layers to effectively capture long-distance relationships among nodes.This stacking not only increases computational burdens but also raises the risk of over-smoothing,which can lead to the neglect of crucial local action features.To address this issue,we propose a novel multi-scale adaptive large kernel graph convolutional network(MSLK-GCN)to effectively aggregate local and global spatio-temporal correlations while maintaining the computational efficiency.The core components of the network include two multi-scale large kernel graph convolution(LKGC)modules,a multi-channel adaptive graph convolution(MAGC)module,and a multi-scale temporal self-attention convolution(MSTC)module.The LKGC module adaptively focuses on active motion regions by utilizing a large convolution kernel and a gating mechanism,effectively capturing long-distance dependencies within the skeleton sequence.Meanwhile,the MAGC module dynamically learns relationships between different joints by adjusting connection weights between nodes.To further enhance the ability to capture temporal dynamics,the MSTC module effectively aggregates the temporal information by integrating Efficient Channel Attention(ECA)with multi-scale convolution.In addition,we use a multi-stream fusion strategy to make full use of different modal skeleton data,including bone,joint,joint motion,and bone motion.Exhaustive experiments on three scale-varying datasets,i.e.,NTU-60,NTU-120,and NW-UCLA,demonstrate that our MSLK-GCN can achieve state-of-the-art performance with fewer parameters.
文摘从单张RGB图像中实现双手的3D交互式网格重建是一项极具挑战性的任务。由于双手之间的相互遮挡以及局部外观相似性较高,导致部分特征提取不够准确,从而丢失了双手之间的交互信息并使重建的手部网格与输入图像出现不对齐等问题。为了解决上述问题,本文首先提出一种包含两个部分的特征交互适应模块,第一部分特征交互在保留左右手分离特征的同时生成两种新的特征表示,并通过交互注意力模块捕获双手的交互特征;第二部分特征适应则是将此交互特征利用交互注意力模块适应到每只手,为左右手特征注入全局上下文信息。其次,引入三层图卷积细化网络结构用于精确回归双手网格顶点,并通过基于注意力机制的特征对齐模块增强顶点特征和图像特征的对齐,从而增强重建的手部网格和输入图像的对齐。同时提出一种新的多层感知机结构,通过下采样和上采样操作学习多尺度特征信息。最后,设计相对偏移损失函数约束双手的空间关系。在InterHand2.6M数据集上的定量和定性实验表明,与现有的优秀方法相比,所提出的方法显著提升了模型性能,其中平均每关节位置误差(Mean Per Joint Position Error,MPJPE)和平均每顶点位置误差(Mean Per Vertex Position Error,MPVPE)分别降低至7.19 mm和7.33 mm。此外,在RGB2Hands和EgoHands数据集上进行泛化性实验,定性实验结果表明所提出的方法具有良好的泛化能力,能够适应不同环境背景下的手部网格重建。
