The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to u...The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.展开更多
Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning mode...Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning models for epidemic forecasting,spatial and temporal variations are captured separately.A unified model is developed to cover all spatio-temporal relations.However,this measure is insufficient for modelling the complex spatio-temporal relations of infectious disease transmission.A dynamic adaptive spatio-temporal graph network(DASTGN)is proposed based on attention mechanisms to improve prediction accuracy.In DASTGN,complex spatio-temporal relations are depicted by adaptively fusing the mixed space-time effects and dynamic space-time dependency structure.This dual-scale model considers the time-specific,space-specific,and direct effects of the propagation process at the fine-grained level.Furthermore,the model characterises impacts from various space-time neighbour blocks under time-varying interventions at the coarse-grained level.The performance comparisons on the three COVID-19 datasets reveal that DASTGN achieves state-of-the-art results with a maximum improvement of 17.092%in the root mean-square error and 11.563%in the mean absolute error.Experimental results indicate that the mechanisms of designing DASTGN can effectively detect some spreading characteristics of COVID-19.The spatio-temporal weight matrices learned in each proposed module reveal diffusion patterns in various scenarios.In conclusion,DASTGN has successfully captured the dynamic spatio-temporal variations of COVID-19,and considering multiple dynamic space-time relationships is essential in epidemic forecasting.展开更多
The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries an...The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other fields.Furthermore,it is important to construct a digital twin system.However,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted accuracy.In this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)layer.Specifically,we fuse the spatial-temporal graph based on the interrelationship of spatial graphs.Simultaneously,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding methods.Subsequently,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)module.The module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like data.AFSTGCN dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted accuracy.Experiments on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.展开更多
Electrocardiogram (ECG) analysis is critical for detecting arrhythmias, but traditional methods struggle with large-scale Electrocardiogram data and rare arrhythmia events in imbalanced datasets. These methods fail to...Electrocardiogram (ECG) analysis is critical for detecting arrhythmias, but traditional methods struggle with large-scale Electrocardiogram data and rare arrhythmia events in imbalanced datasets. These methods fail to perform multi-perspective learning of temporal signals and Electrocardiogram images, nor can they fully extract the latent information within the data, falling short of the accuracy required by clinicians. Therefore, this paper proposes an innovative hybrid multimodal spatiotemporal neural network to address these challenges. The model employs a multimodal data augmentation framework integrating visual and signal-based features to enhance the classification performance of rare arrhythmias in imbalanced datasets. Additionally, the spatiotemporal fusion module incorporates a spatiotemporal graph convolutional network to jointly model temporal and spatial features, uncovering complex dependencies within the Electrocardiogram data and improving the model’s ability to represent complex patterns. In experiments conducted on the MIT-BIH arrhythmia dataset, the model achieved 99.95% accuracy, 99.80% recall, and a 99.78% F1 score. The model was further validated for generalization using the clinical INCART arrhythmia dataset, and the results demonstrated its effectiveness in terms of both generalization and robustness.展开更多
Accurate traffic pattern prediction in largescale networks is of great importance for intelligent system management and automatic resource allocation.System-level mobile traffic forecasting has significant challenges ...Accurate traffic pattern prediction in largescale networks is of great importance for intelligent system management and automatic resource allocation.System-level mobile traffic forecasting has significant challenges due to the tremendous temporal and spatial dynamics introduced by diverse Internet user behaviors and frequent traffic migration.Spatialtemporal graph modeling is an efficient approach for analyzing the spatial relations and temporal trends of mobile traffic in a large system.Previous research may not reflect the optimal dependency by ignoring inter-base station dependency or pre-determining the explicit geological distance as the interrelationship of base stations.To overcome the limitations of graph structure,this study proposes an adaptive graph convolutional network(AGCN)that captures the latent spatial dependency by developing self-adaptive dependency matrices and acquires temporal dependency using recurrent neural networks.Evaluated on two mobile network datasets,the experimental results demonstrate that this method outperforms other baselines and reduces the mean absolute error by 3.7%and 5.6%compared to time-series based approaches.展开更多
In recent years,aquaculture has developed rapidly,especially in coastal and open ocean areas.In practice,water quality prediction is of critical importance.However,traditional water quality prediction models face limi...In recent years,aquaculture has developed rapidly,especially in coastal and open ocean areas.In practice,water quality prediction is of critical importance.However,traditional water quality prediction models face limitations in handling complex spatiotemporal patterns.To address this challenge,a prediction model was proposed for water quality,namely an adaptive multi-channel temporal graph convolutional network(AMTGCN).The AMTGCN integrates adaptive graph construction,multi-channel spatiotemporal graph convolutional network,and fusion layers,and can comprehensively capture the spatial relationships and spatiotemporal patterns in aquaculture water quality data.Onsite aquaculture water quality data and the metrics MAE,RMSE,MAPE,and R^(2) were collected to validate the AMTGCN.The results show that the AMTGCN presents an average improvement of 34.01%,34.59%,36.05%,and 17.71%compared to LSTM,respectively;an average improvement of 64.84%,56.78%,64.82%,and 153.16%compared to the STGCN,respectively;an average improvement of 55.25%,48.67%,57.01%,and 209.00%compared to GCN-LSTM,respectively;and an average improvement of 7.05%,5.66%,7.42%,and 2.47%compared to TCN,respectively.This indicates that the AMTGCN,integrating the innovative structure of adaptive graph construction and multi-channel spatiotemporal graph convolutional network,could provide an efficient solution for water quality prediction in aquaculture.展开更多
The relation is a semantic expression relevant to two named entities in a sentence.Since a sentence usually contains several named entities,it is essential to learn a structured sentence representation that encodes de...The relation is a semantic expression relevant to two named entities in a sentence.Since a sentence usually contains several named entities,it is essential to learn a structured sentence representation that encodes dependency information specific to the two named entities.In related work,graph convolutional neural networks are widely adopted to learn semantic dependencies,where a dependency tree initializes the adjacency matrix.However,this approach has two main issues.First,parsing a sentence heavily relies on external toolkits,which can be errorprone.Second,the dependency tree only encodes the syntactical structure of a sentence,which may not align with the relational semantic expression.In this paper,we propose an automatic graph learningmethod to autonomously learn a sentence’s structural information.Instead of using a fixed adjacency matrix initialized by a dependency tree,we introduce an Adaptive Adjacency Matrix to encode the semantic dependency between tokens.The elements of thismatrix are dynamically learned during the training process and optimized by task-relevant learning objectives,enabling the construction of task-relevant semantic dependencies within a sentence.Our model demonstrates superior performance on the TACRED and SemEval 2010 datasets,surpassing previous works by 1.3%and 0.8%,respectively.These experimental results show that our model excels in the relation extraction task,outperforming prior models.展开更多
In order to address the issues of predefined adjacency matrices inadequately representing information in road networks,insufficiently capturing spatial dependencies of traffic networks,and the potential problem of exc...In order to address the issues of predefined adjacency matrices inadequately representing information in road networks,insufficiently capturing spatial dependencies of traffic networks,and the potential problem of excessive smoothing or neglecting initial node information as the layers of graph convolutional neural networks increase,thus affecting traffic prediction performance,this paper proposes a prediction model based on Adaptive Multi-channel Graph Convolutional Neural Networks(AMGCN).The model utilizes an adaptive adjacency matrix to automatically learn implicit graph structures from data,introduces a mixed skip propagation graph convolutional neural network model,which retains the original node states and selectively acquires outputs of convolutional layers,thus avoiding the loss of node initial states and comprehensively capturing spatial correlations of traffic flow.Finally,the output is fed into Long Short-Term Memory networks to capture temporal correlations.Comparative experiments on two real datasets validate the effectiveness of the proposed model.展开更多
With the advent of deep learning,various deep neural network architectures have been proposed to capture the complex spatio-temporal dependencies in traffic data.This paper introduces a novel Deep Bi-directional Adapt...With the advent of deep learning,various deep neural network architectures have been proposed to capture the complex spatio-temporal dependencies in traffic data.This paper introduces a novel Deep Bi-directional Adaptive Gating Graph Convolutional Network(DBAG-GCN)model for spatio-temporal traffic forecasting.The proposed model leverages the power of graph convolutional networks to capture the spatial dependencies in the road network topology and incorporates bi-directional gating mechanisms to control the information flow adaptively.Furthermore,we introduce a multi-scale temporal convolution module to capture multi-scale temporal dynamics and a contextual attention mechanism to integrate external factors such as weather conditions and event information.Extensive experiments on real-world traffic datasets demonstrate the superior performance of DBAG-GCN compared to state-of-the-art baselines,achieving significant improvements in prediction accuracy and computational efficiency.The DBAG-GCN model provides a powerful and flexible framework for spatio-temporal traffic forecasting,paving the way for intelligent transportation management and urban planning.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The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.
基金Youth Innovation Promotion Association CAS,Grant/Award Number:2021103Strategic Priority Research Program of Chinese Academy of Sciences,Grant/Award Number:XDC02060500。
文摘Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning models for epidemic forecasting,spatial and temporal variations are captured separately.A unified model is developed to cover all spatio-temporal relations.However,this measure is insufficient for modelling the complex spatio-temporal relations of infectious disease transmission.A dynamic adaptive spatio-temporal graph network(DASTGN)is proposed based on attention mechanisms to improve prediction accuracy.In DASTGN,complex spatio-temporal relations are depicted by adaptively fusing the mixed space-time effects and dynamic space-time dependency structure.This dual-scale model considers the time-specific,space-specific,and direct effects of the propagation process at the fine-grained level.Furthermore,the model characterises impacts from various space-time neighbour blocks under time-varying interventions at the coarse-grained level.The performance comparisons on the three COVID-19 datasets reveal that DASTGN achieves state-of-the-art results with a maximum improvement of 17.092%in the root mean-square error and 11.563%in the mean absolute error.Experimental results indicate that the mechanisms of designing DASTGN can effectively detect some spreading characteristics of COVID-19.The spatio-temporal weight matrices learned in each proposed module reveal diffusion patterns in various scenarios.In conclusion,DASTGN has successfully captured the dynamic spatio-temporal variations of COVID-19,and considering multiple dynamic space-time relationships is essential in epidemic forecasting.
基金supported by the China Scholarship Council and the CERNET Innovation Project under grant No.20170111.
文摘The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other fields.Furthermore,it is important to construct a digital twin system.However,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted accuracy.In this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)layer.Specifically,we fuse the spatial-temporal graph based on the interrelationship of spatial graphs.Simultaneously,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding methods.Subsequently,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)module.The module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like data.AFSTGCN dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted accuracy.Experiments on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.
基金supported by The Henan Province Science and Technology Research Project(242102211046)the Key Scientific Research Project of Higher Education Institutions in Henan Province(25A520039)+1 种基金theNatural Science Foundation project of Zhongyuan Institute of Technology(K2025YB011)the Zhongyuan University of Technology Graduate Education and Teaching Reform Research Project(JG202424).
文摘Electrocardiogram (ECG) analysis is critical for detecting arrhythmias, but traditional methods struggle with large-scale Electrocardiogram data and rare arrhythmia events in imbalanced datasets. These methods fail to perform multi-perspective learning of temporal signals and Electrocardiogram images, nor can they fully extract the latent information within the data, falling short of the accuracy required by clinicians. Therefore, this paper proposes an innovative hybrid multimodal spatiotemporal neural network to address these challenges. The model employs a multimodal data augmentation framework integrating visual and signal-based features to enhance the classification performance of rare arrhythmias in imbalanced datasets. Additionally, the spatiotemporal fusion module incorporates a spatiotemporal graph convolutional network to jointly model temporal and spatial features, uncovering complex dependencies within the Electrocardiogram data and improving the model’s ability to represent complex patterns. In experiments conducted on the MIT-BIH arrhythmia dataset, the model achieved 99.95% accuracy, 99.80% recall, and a 99.78% F1 score. The model was further validated for generalization using the clinical INCART arrhythmia dataset, and the results demonstrated its effectiveness in terms of both generalization and robustness.
基金supported by the National Natural Science Foundation of China(61975020,62171053)。
文摘Accurate traffic pattern prediction in largescale networks is of great importance for intelligent system management and automatic resource allocation.System-level mobile traffic forecasting has significant challenges due to the tremendous temporal and spatial dynamics introduced by diverse Internet user behaviors and frequent traffic migration.Spatialtemporal graph modeling is an efficient approach for analyzing the spatial relations and temporal trends of mobile traffic in a large system.Previous research may not reflect the optimal dependency by ignoring inter-base station dependency or pre-determining the explicit geological distance as the interrelationship of base stations.To overcome the limitations of graph structure,this study proposes an adaptive graph convolutional network(AGCN)that captures the latent spatial dependency by developing self-adaptive dependency matrices and acquires temporal dependency using recurrent neural networks.Evaluated on two mobile network datasets,the experimental results demonstrate that this method outperforms other baselines and reduces the mean absolute error by 3.7%and 5.6%compared to time-series based approaches.
基金funded by the National Key Research and Development Program of China:Sino-Malta Fund 2022“Autonomous Biomimetic Underwater Vehicle for Digital Cage Monitoring”(Grant No.2022YFE0107100).
文摘In recent years,aquaculture has developed rapidly,especially in coastal and open ocean areas.In practice,water quality prediction is of critical importance.However,traditional water quality prediction models face limitations in handling complex spatiotemporal patterns.To address this challenge,a prediction model was proposed for water quality,namely an adaptive multi-channel temporal graph convolutional network(AMTGCN).The AMTGCN integrates adaptive graph construction,multi-channel spatiotemporal graph convolutional network,and fusion layers,and can comprehensively capture the spatial relationships and spatiotemporal patterns in aquaculture water quality data.Onsite aquaculture water quality data and the metrics MAE,RMSE,MAPE,and R^(2) were collected to validate the AMTGCN.The results show that the AMTGCN presents an average improvement of 34.01%,34.59%,36.05%,and 17.71%compared to LSTM,respectively;an average improvement of 64.84%,56.78%,64.82%,and 153.16%compared to the STGCN,respectively;an average improvement of 55.25%,48.67%,57.01%,and 209.00%compared to GCN-LSTM,respectively;and an average improvement of 7.05%,5.66%,7.42%,and 2.47%compared to TCN,respectively.This indicates that the AMTGCN,integrating the innovative structure of adaptive graph construction and multi-channel spatiotemporal graph convolutional network,could provide an efficient solution for water quality prediction in aquaculture.
基金supported by the Technology Projects of Guizhou Province under Grant[2024]003National Natural Science Foundation of China(GrantNos.62166007,62066008,62066007)Guizhou Provincial Science and Technology Projects under Grant No.ZK[2023]300.
文摘The relation is a semantic expression relevant to two named entities in a sentence.Since a sentence usually contains several named entities,it is essential to learn a structured sentence representation that encodes dependency information specific to the two named entities.In related work,graph convolutional neural networks are widely adopted to learn semantic dependencies,where a dependency tree initializes the adjacency matrix.However,this approach has two main issues.First,parsing a sentence heavily relies on external toolkits,which can be errorprone.Second,the dependency tree only encodes the syntactical structure of a sentence,which may not align with the relational semantic expression.In this paper,we propose an automatic graph learningmethod to autonomously learn a sentence’s structural information.Instead of using a fixed adjacency matrix initialized by a dependency tree,we introduce an Adaptive Adjacency Matrix to encode the semantic dependency between tokens.The elements of thismatrix are dynamically learned during the training process and optimized by task-relevant learning objectives,enabling the construction of task-relevant semantic dependencies within a sentence.Our model demonstrates superior performance on the TACRED and SemEval 2010 datasets,surpassing previous works by 1.3%and 0.8%,respectively.These experimental results show that our model excels in the relation extraction task,outperforming prior models.
文摘In order to address the issues of predefined adjacency matrices inadequately representing information in road networks,insufficiently capturing spatial dependencies of traffic networks,and the potential problem of excessive smoothing or neglecting initial node information as the layers of graph convolutional neural networks increase,thus affecting traffic prediction performance,this paper proposes a prediction model based on Adaptive Multi-channel Graph Convolutional Neural Networks(AMGCN).The model utilizes an adaptive adjacency matrix to automatically learn implicit graph structures from data,introduces a mixed skip propagation graph convolutional neural network model,which retains the original node states and selectively acquires outputs of convolutional layers,thus avoiding the loss of node initial states and comprehensively capturing spatial correlations of traffic flow.Finally,the output is fed into Long Short-Term Memory networks to capture temporal correlations.Comparative experiments on two real datasets validate the effectiveness of the proposed model.
基金supported by the National Natural Science Foundation of China(Nos.62202247 and 62306073)the National Key Research and Development Program of China(No.2022ZD0115303).
文摘With the advent of deep learning,various deep neural network architectures have been proposed to capture the complex spatio-temporal dependencies in traffic data.This paper introduces a novel Deep Bi-directional Adaptive Gating Graph Convolutional Network(DBAG-GCN)model for spatio-temporal traffic forecasting.The proposed model leverages the power of graph convolutional networks to capture the spatial dependencies in the road network topology and incorporates bi-directional gating mechanisms to control the information flow adaptively.Furthermore,we introduce a multi-scale temporal convolution module to capture multi-scale temporal dynamics and a contextual attention mechanism to integrate external factors such as weather conditions and event information.Extensive experiments on real-world traffic datasets demonstrate the superior performance of DBAG-GCN compared to state-of-the-art baselines,achieving significant improvements in prediction accuracy and computational efficiency.The DBAG-GCN model provides a powerful and flexible framework for spatio-temporal traffic forecasting,paving the way for intelligent transportation management and urban planning.
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。
