Search Results for author: Haiyong Luo

Found 10 papers, 4 papers with code

OccupancyDETR: Making Semantic Scene Completion as Straightforward as Object Detection

1 code implementation15 Sep 2023 Yupeng Jia, Jie He, Runze Chen, Fang Zhao, Haiyong Luo

Visual-based 3D semantic occupancy perception (also known as 3D semantic scene completion) is a new perception paradigm for robotic applications like autonomous driving.

3D Semantic Scene Completion Autonomous Driving +4

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

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.

A Light-weight Deep Human Activity Recognition Algorithm Using Multi-knowledge Distillation

no code implementations6 Jul 2021 Runze Chen, Haiyong Luo, Fang Zhao, Xuechun Meng, Zhiqing Xie, Yida Zhu

The comparative experiments of knowledge distillation on six public datasets also demonstrate that the SMLDist outperforms other advanced knowledge distillation methods of students' performance, which verifies the good generalization of the SMLDist on other classification tasks, including but not limited to HAR.

Classification Human Activity Recognition +2

Bridge the Vision Gap from Field to Command: A Deep Learning Network Enhancing Illumination and Details

no code implementations20 Jan 2021 Zhuqing Jiang, Chang Liu, Ya'nan Wang, Kai Li, Aidong Men, Haiying Wang, Haiyong Luo

With the goal of tuning up the brightness, low-light image enhancement enjoys numerous applications, such as surveillance, remote sensing and computational photography.

Low-Light Image Enhancement

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