Search Results for author: Chenxing Wang

Found 8 papers, 2 papers with code

When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks

1 code implementation IEEE 39th International Conference on Data Engineering (ICDE) 2023 Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Bingbing Xu, Liang Zeng, Chenxing Wang

To capture these intricate dependencies, spatio-temporal networks, such as recurrent neural networks with graph convolution networks, graph convolution networks with temporal convolution networks, and temporal attention networks with full graph attention networks, are applied.

Graph Attention Traffic Prediction

3D Single-pixel imaging with active sampling patterns and learning based reconstruction

no code implementations6 Sep 2022 Xinyue Ma, Chenxing Wang

Single-pixel imaging (SPI) is significant for applications constrained by transmission bandwidth or lighting band, where 3D SPI can be further realized through capturing signals carrying depth.

3D Reconstruction

Fine-Grained Trajectory-based Travel Time Estimation for Multi-city Scenarios Based on Deep Meta-Learning

1 code implementation20 Jan 2022 Chenxing Wang, Fang Zhao, Haichao Zhang, Haiyong Luo, Yanjun Qin, Yuchen Fang

To tackle these challenges, we propose a meta learning based framework, MetaTTE, to continuously provide accurate travel time estimation over time by leveraging well-designed deep neural network model called DED, which consists of Data preprocessing module and Encoder-Decoder network module.

Decoder Meta-Learning +1

Spatio-Temporal meets Wavelet: Disentangled Traffic Flow Forecasting via Efficient Spectral Graph Attention Network

no code implementations6 Dec 2021 Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Bingbing Xu, Chenxing Wang, Liang Zeng

Traffic forecasting is crucial for public safety and resource optimization, yet is very challenging due to three aspects: i) current existing works mostly exploit intricate temporal patterns (e. g., the short-term thunderstorm and long-term daily trends) within a single method, which fail to accurately capture spatio-temporal dependencies under different schemas; ii) the under-exploration of the graph positional encoding limit the extraction of spatial information in the commonly used full graph attention network; iii) the quadratic complexity of the full graph attention introduces heavy computational needs.

Graph Attention Time Series Analysis

CDGNet: A Cross-Time Dynamic Graph-based Deep Learning Model for Traffic Forecasting

no code implementations6 Dec 2021 Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Liang Zeng, Bo Hui, Chenxing Wang

Besides, we propose a novel encoder-decoder architecture to incorporate the cross-time dynamic graph-based GCN for multi-step traffic forecasting.


DMGCRN: Dynamic Multi-Graph Convolution Recurrent Network for Traffic Forecasting

no code implementations4 Dec 2021 Yanjun Qin, Yuchen Fang, Haiyong Luo, Fang Zhao, Chenxing Wang

In this paper, we propose a novel dynamic multi-graph convolution recurrent network (DMGCRN) to tackle above issues, which can model the spatial correlations of distance, the spatial correlations of structure, and the temporal correlations simultaneously.

Single-shot fringe projection profilometry based on Deep Learning and Computer Graphics

no code implementations4 Jan 2021 Fanzhou Wang, Chenxing Wang, Qingze Guan

Multiple works have applied deep learning to fringe projection profilometry (FPP) in recent years.

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