检索结果-内蒙古大学图书馆

Multiscale information enhanced spatial-temporal graph convolutional network for multivariate traffic flow forecasting via magnifying perceptual scope

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartB期136卷

作者： Zheng, Xinyu Shao, Haidong Yan, Shen Xiao, Yiming Liu, Bin Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Peoples R China Univ Strathclyde Dept Management Sci Glasgow G1 1XQ Scotland

graph convolutional Networks (GCNs), which can model data in non-Euclidean space, have received extensive attention in multivariate traffic flow forecasting in recent years. However, most of the existing GCN forecasting methods rely on adjacency matrices defined by road network knowledge, which cannot accurately capture the inherent spatial-temporal dependencies of complex multivariate traffic flows. In this paper, a multiscale information enhanced spatial-temporal GCN is proposed, which utilizes an adaptive graph learning layer to automatically extract the dependencies between variables and constructs adaptive adjacency matrices for different datasets. In addition, an information enhancement module is introduced to magnify perceptual scope of the model and strengthen the connection between information, so that the spatial-temporal graph convolution module can better capture the spatial-temporal dependencies in the traffic flow. We conducted experiments on multivariate traffic flow data from three different real-world scenarios to evaluate the effectiveness of the proposed method. Comparative experimental results consistently show that the proposed method outperforms existing GCNs, achieving an average improvement of 21.16% on Mean Absolute Error (MAE), 18.87% on Root Mean Square Error (RMSE), and 8.27% on R2 score (R2) in the three datasets, respectively, and the results demonstrate its excellent forecasting performance in the field of multivariate traffic flow forecasting.

关键词： Multivariate traffic flow forecasting Multiscale information spatial-temporal graph convolution Information enhancement module Adaptive graph learning

来源：评论

学校读者我要写书评

暂无评论

Memory Enhanced spatial-temporal graph convolutional Autoencoder for Human-Related Video Anomaly Detection 5th

Memory Enhanced Spatial-Temporal Graph Convolutional Autoenc...

引用

5th Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Luo, Sibo Wang, Shangshang Wu, Yuan Jin, Cheng Fudan Univ Sch Comp Sci Shanghai Peoples R China

ISBN: (纸本)9783031189128;9783031189135

Human-related video anomaly detection is a challenging problem due to unclear definitions of anomalies and insufficient training data. Pose-based methods have attracted widespread attention by exploiting highly structured skeleton data that are robust to background noise and illumination changes. However, existing methods use recurrent neural network to extract temporal information while ignoring the spatial dependencies between skeleton joints, which are crucial to reason behaviors. Additionally, commonly-used methods are expected to produce larger reconstruction errors for anomalies than normal samples to achieve anomaly detection. But in practice, due to the strong generalization ability of these models, they fail to obtain significant reconstruction errors for abnormal samples, resulting in missing anomalies. In this paper, we propose a novel framework Memory Enhanced spatial-temporal graph convolutional Autoencoder(Mem-STGCAE) to address these problems. We use spatial-temporal graph convolution as an encoder to capture discriminative features in spatial and temporal domains. We enhance the autoencoder with a memory module that records normal patterns. The encoded representation is used as a query to retrieve the most relevant patterns. Thus, the decoder reconstructs anomalies using normal patterns, resulting in significant reconstruction errors. Different from traditional autoencoders, two branches of decoder are introduced to reconstruct past and predict future pose sequences respectively. Extensive experiments on two challenging video anomaly datasets, ShanghaiTech and IITB-Corridor, show that our proposed network outperforms other state-of-the-art methods.

关键词： Anomaly detection spatial-temporal graph convolution Memory networks Skeleton

来源：评论

学校读者我要写书评

暂无评论

Multi-View spatial-temporal graph convolutional Networks With Domain Generalization for Sleep Stage Classification

引用

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2021年 29卷 1977-1986页

作者： Jia, Ziyu Lin, Youfang Wang, Jing Ning, Xiaojun He, Yuanlai Zhou, Ronghao Zhou, Yuhan Lehman, Li-Wei H. Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China MIT Inst Med Engn & Sci Cambridge MA 02139 USA

Sleep stage classification is essential for sleep assessment and disease diagnosis. Although previous attempts to classify sleep stages have achieved high classification performance, several challenges remain open: 1) How to effectively utilize time-varying spatial and temporal features from multi-channel brain signals remains challenging. Prior works have not been able to fully utilize the spatial topological information among brain regions. 2) Due to the many differences found in individual biological signals, how to overcome the differences of subjects and improve the generalization of deep neural networks is important. 3) Most deep learning methods ignore the interpretability of the model to the brain. To address the above challenges, we propose a multi-view spatial-temporal graph convolutional networks (MSTGCN) with domain generalization for sleep stage classification. Specifically, we construct two brain view graphs for MSTGCN based on the functional connectivity and physical distance proximity of the brain regions. The MSTGCN consists of graph convolutions for extracting spatial features and temporal convolutions for capturing the transition rules among sleep stages. In addition, attention mechanism is employed for capturing the most relevant spatial-temporal information for sleep stage classification. Finally, domain generalization and MSTGCN are integrated into a unified framework to extract subject-invariant sleep features. Experiments on two public datasets demonstrate that the proposed model outperforms the state-of-the-art baselines.

关键词： Sleep Brain modeling convolution Feature extraction convolutional neural networks Adaptation models Transfer learning Sleep stage classification spatial-temporal graph convolution transfer learning domain generalization

来源：评论

学校读者我要写书评

暂无评论

PoseRL-Net: human pose analysis for motion training guided by robot vision

引用

FRONTIERS IN NEUROROBOTICS 2025年 19卷 1531894页

作者： Liu, Bin Wang, Hui Shanghai Jianqiao Univ Coll Educ Dept Phys Educ Shanghai Peoples R China

Objective To address the limitations of traditional methods in human pose recognition, such as occlusions, lighting variations, and motion continuity, particularly in complex dynamic environments for seamless human-robot *** We propose PoseRL-Net, a deep learning-based pose recognition model that enhances accuracy and robustness in human pose estimation. PoseRL-Net integrates multiple components, including a spatial-temporal graph convolutional Network (STGCN), attention mechanism, Gated Recurrent Unit (GRU) module, pose refinement, and symmetry constraints. The STGCN extracts spatial and temporal features, the attention mechanism focuses on key pose features, the GRU ensures temporal consistency, and the refinement and symmetry constraints improve structural plausibility and *** Extensive experiments conducted on the Human3.6M and MPI-INF-3DHP datasets demonstrate that PoseRL-Net outperforms existing state-of-the-art models on key metrics such as MPIPE and P-MPIPE, showcasing superior performance across various pose recognition *** PoseRL-Net not only improves pose estimation accuracy but also provides crucial support for intelligent decision-making and motion planning in robots operating in dynamic and complex scenarios, offering significant practical value for collaborative robotics.

关键词： human pose estimation 3D skeleton modeling spatial-temporal graph convolution attention mechanism robot-assisted motion analysis

来源：评论

学校读者我要写书评

暂无评论

A Skeleton-Based Rehabilitation Exercise Assessment System With Rotation Invariance

引用

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2023年 31卷 2612-2621页

作者： Zheng, Kaili Wu, Ji Zhang, Jialin Guo, Chenyi Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Tsinghua Univ Inst Precis Med Beijing 100084 Peoples R China

Automated exercise assessment is of great importance for patients under rehabilitation exercise who require professional guidance. Among the existing approaches, the skeleton-based assessment model that classifies the correctness of the exercise has attracted much attention due to its relative ease of implementation and convenience in use. However, there are two problems with this approach. The first problem is its sensitivity to the orientation of the human skeleton. To solve this problem, we propose a novel rotation-invariant descriptor, the dot product matrix of the human skeleton, and prove mathematically that this descriptor discards only the orientation message that we do not expect while preserving all other useful information. Lack of feedback from the system is the second problem, because the exercisers do not know which parts of their exercises are incorrect. Therefore, we develop a visualization method for our system based on Gradient-Weighted Class Activation Mapping (Grad-CAM) and an quantitative metric called Overlap Ratio (OvR) to measure the quality of the visualization result. To demonstrate the effect of our method, we conduct experiments on two public datasets and a self-generated push-up dataset. The experimental results show that our rotation-invariant descriptor can achieve absolute robustness to orientation even under severe angle perturbations. In terms of accuracy and OvR, our method even outperforms previous works in most cases, indicating that the rotation-invariant descriptor helps the assessment model to extract more stable features. The visualization results are also informative to correct the movements;some examples are presented in this paper. The code of this paper and our push-up dataset are publicly available at https://***/Kelly510/RehabExerAssess.

关键词： Index Terms- Automated assessment physical rehabilitation rotation invariance spatial-temporal graph convolution

来源：评论

学校读者我要写书评

暂无评论

Brainnetformer: Decoding Brain Cognitive States with spatial-temporal Cross Attention 48

Brainnetformer: Decoding Brain Cognitive States with Spatial...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Sheng, Leheng Wang, Wenhan Shi, Zhiyi Zhan, Jichao Kong, Youyong Southeast University Chien-Shiung Wu College China Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing China

ISBN: (纸本)9781728163277

Learning about the cognitive state of the brain has always been a popular topic. Based on the fact that fluctuations of brain signals and functional connectome (FC) relate to specific human behaviors, deep learning based methods have shown promising results on the prediction of such behaviors by analyzing biological signals. Existing methods either model from static perspectives or apply spatial-temporal graph convolution to extract dynamic properties. However, the static information and dynamic information can reflect global brain activities and local brain activities respectively. Thus, we propose BrainNetFormer to incorporate both static and dynamic properties for human behavior prediction. To be specific, a spatial cross attention module and a temporal cross attention module are introduced for information fusion. In addition, since a specific behavior of subjects can be decomposed into a series of subtasks, we introduce a sub-task regularization loss to assist in training and empower the model to recognize subtasks at each moment. Experiments on the HCP-Task dataset demonstrate the superior performance of the proposed model. © 2023 IEEE.

关键词： brain states decoding cross attention functional MRI images spatial-temporal graph convolution

来源：评论

学校读者我要写书评

暂无评论

spatial-temporal graph neural network based on node attention

引用

APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2022年第2期7卷 703-712页

作者： Li, Qiang Wan, Jun Zhang, Wucong Kweh, Qian Long South China Univ Technol Sch Elect & Informat Engn Guangzhou Peoples R China Midea Real Estate Holding Ltd Midea Intelligent Life Res Inst Foshan Peoples R China Canadian Univ Dubai Saudi Arabia

Recently, the method of using graph neural network based on skeletons for action recognition has become more and more popular, due to the fact that a skeleton can carry very intuitive and rich action information, without being affected by background, light and other factors. The spatial-temporal graph convolutional neural network (ST-GCN) is a dynamic skeleton model that automatically learns spatial-temporal model from data, which not only has stronger expression ability, but also has stronger generalisation ability, showing remarkable results on public data sets. However, the ST-GCN network directly learns the information of adjacent nodes (local information), and is insufficient in learning the relations of non-adjacent nodes (global information), such as clapping action that requires learning the related information of non-adjacent nodes. Therefore, this paper proposes an ST-GCN based on node attention (NA-STGCN), so as to solve the problem of insufficient global information in ST-GCN by introducing node attention module to explicitly model the interdependence between global nodes. The experimental results on the NTU-RGB+D set show that the node attention module can effectively improve the accuracy and feature representation ability of the existing algorithms, and obviously improve the recognition effect of the actions that need global information.

关键词： Action recognition skeletons spatial-temporal graph convolution attention mechanism

来源：评论

学校读者我要写书评

暂无评论

Lip graph Assisted Audio-Visual Speech Recognition Using Bidirectional Synchronous Fusion 21

Lip Graph Assisted Audio-Visual Speech Recognition Using Bid...

引用

Interspeech Conference

作者： Liu, Hong Chen, Zhan Yang, Bing Peking Univ Shenzhen Grad Sch Key Lab Machine Percept Shenzhen Peoples R China

ISBN: (纸本)9781713820697

Current studies have shown that extracting representative visual features and efficiently fusing audio and visual modalities are vital for audio-visual speech recognition (AVSR), but these are still challenging. To this end, we propose a lip graph assisted AVSR method with bidirectional synchronous fusion. First, a hybrid visual stream combines the image branch and graph branch to capture discriminative visual features. Specially, the lip graph exploits the natural and dynamic connections between the lip key points to model the lip shape, and the temporal evolution of the lip graph is captured by the graph convolutional networks followed by bidirectional gated recurrent units. Second, the hybrid visual stream is combined with the audio stream by an attention-based bidirectional synchronous fusion which allows bidirectional information interaction to resolve the asynchrony between the two modalities during fusion. The experimental results on LRW-BBC dataset show that our method outperforms the end-to-end AVSR baseline method in both clean and noisy conditions.

关键词： Audio-visual speech recognition spatial-temporal graph convolution deep learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：