Long Wei
Zhejiang University
10 Papers
14 Citations
Long Wei is an academic researcher from Zhejiang University. The author has contributed to research in topics: Computer science & Graph (abstract data type). The author has an hindex of 4, co-authored 10 publications. Previous affiliations of Long Wei include Alibaba Group.
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Papers
Dynamic Spatio-Temporal Graph-Based CNNs for Traffic Flow Prediction
Chen Ken,Chen Fei,Baisheng Lai,Zhongming Jin,Yong Liu,Li Kai,Long Wei,Pengfei Wang,Tang Yandong,Jianqiang Huang,Xian-Sheng Hua +10 more
TL;DR: DST-GCNN is a two stream network that learns expressive features to represent spatio-temporal structures and predicts future traffic flows from surveillance video data and achieves competitive performances compared with the other state-of-the-art methods.
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•Posted Content
Dynamic Spatio-temporal Graph-based CNNs for Traffic Prediction
Chen Ken,Chen Fei,Baisheng Lai,Zhongming Jin,Yong Liu,Li Kai,Long Wei,Pengfei Wang,Tang Yandong,Jianqiang Huang,Xian-Sheng Hua +10 more
TL;DR: This paper presents dynamic spatio-temporal graph-based CNNs (DST-GCNNs) by learning expressive features to represent spatio,temporal structures and predict future traffic flows from surveillance video data using a two stream network.
SIF: Self-Inspirited Feature Learning for Person Re-Identification
Long Wei,Wei Zhenyong,Zhongming Jin,Zhengxu Yu,Jianqiang Huang,Deng Cai,Xiaofei He,Xian-Sheng Hua +7 more
TL;DR: This paper proposes a Self-Inspirited Feature Learning (SIF) method to enhance the performance of given ReID networks from the viewpoint of optimization, and designs a simple adversarial learning scheme to encourage a network to learn more discriminative person representation.
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MaCAR: Urban Traffic Light Control via Active Multi-agent Communication and Action Rectification.
Zhengxu Yu,Shuxian Liang,Shuxian Liang,Long Wei,Zhongming Jin,Jianqiang Huang,Deng Cai,Xiaofei He,Xian-Sheng Hua +8 more
- 09 Jul 2020
TL;DR: This work proposes a novel Multi-agent Communication and Action Rectification (MaCAR) framework which enables active communication between agents by considering the impact of synchronous actions of agents and outperforms state-of-the-art methods on both synthetic and real-world datasets.
Dual Graph for Traffic Forecasting
Long Wei,Zhengxu Yu,Zhongming Jin,Liang Xie,Jianqiang Huang,Deng Cai,Xiaofei He,Xian-Sheng Hua +7 more
TL;DR: A novel dual graph framework, called DualGraph, is proposed, to model the propagation behavior of traffic on road networks, and shows that even for node or edge traffic forecasting alone, the model still outperforms compared ones, especially for long term prediction.
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