1 code implementation • 21 Mar 2024 • Wei Chen, Yuxuan Liang, Yuanshao Zhu, Yanchuan Chang, Kang Luo, Haomin Wen, Lei LI, Yanwei Yu, Qingsong Wen, Chao Chen, Kai Zheng, Yunjun Gao, Xiaofang Zhou, Yu Zheng
In this paper, we present a comprehensive review of the development and recent advances in deep learning for trajectory computing (DL4Traj).
no code implementations • 18 Mar 2024 • Xiang Li, Chaofan Fu, Zhongying Zhao, Guanjie Zheng, Chao Huang, Junyu Dong, Yanwei Yu
Nevertheless, these approaches still grapple with two significant shortcomings: (1) Insufficient modeling and exploitation of the impact of various behavior patterns formed by multiplex relations between users and items on representation learning, and (2) ignoring the effect of different relations in the behavior patterns on the target relation in recommender system scenarios.
no code implementations • 12 Mar 2024 • Zhanyu Liu, Ke Hao, Guanjie Zheng, Yanwei Yu
Different from previous methods, our proposed framework aims to generate a condensed dataset that matches the surrogate objectives in both the time and frequency domains.
1 code implementation • 1 Feb 2024 • Zhanyu Liu, Guanjie Zheng, Yanwei Yu
Next, the traffic data of the data-scarce target city could query the traffic pattern bank, facilitating the aggregation of meta-knowledge.
no code implementations • 25 Aug 2023 • Yibo Wang, Yunhu Ye, Yuanpeng Mao, Yanwei Yu, Yuanping Song
Text segmentation tasks have a very wide range of application values, such as image editing, style transfer, watermark removal, etc. However, existing public datasets are of poor quality of pixel-level labels that have been shown to be notoriously costly to acquire, both in terms of money and time.
1 code implementation • 17 Aug 2023 • Zhanyu Liu, Guanjie Zheng, Yanwei Yu
Then, the traffic data of the data-scarce city could query the traffic pattern bank and explicit relations between them are constructed.
1 code implementation • 11 Feb 2023 • Wei Chen, Chao Huang, Yanwei Yu, Yongguo Jiang, Junyu Dong
Trajectory-User Linking (TUL) is crucial for human mobility modeling by linking diferent trajectories to users with the exploration of complex mobility patterns.
1 code implementation • 12 Aug 2022 • Pengyang Yu, Chaofan Fu, Yanwei Yu, Chao Huang, Zhongying Zhao, Junyu Dong
Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous network data, ranging from link prediction to node classification.
1 code implementation • 12 Jul 2022 • Yuhao Yang, Chao Huang, Lianghao Xia, Yuxuan Liang, Yanwei Yu, Chenliang Li
Further ablation studies validate the effectiveness of our model design and benefits of the new MBHT framework.
1 code implementation • 8 May 2022 • Wei Chen, Shuzhe Li, Chao Huang, Yanwei Yu, Yongguo Jiang, Junyu Dong
In this paper, we propose a novel Mutual distillation learning network to solve the TUL problem for sparse check-in mobility data, named MainTUL.
1 code implementation • 20 Apr 2022 • Zhongqiang Gao, Chuanqi Cheng, Yanwei Yu, Lei Cao, Chao Huang, Junyu Dong
We first categorize the temporal motifs based on their distinct properties, and then design customized algorithms that offer efficient strategies to exactly count the motif instances of each category.
1 code implementation • 4 Jul 2021 • Zhihao Wang, Yanwei Yu, Yibo Wang, Haixu Long, Fazheng Wang
Offline Chinese handwriting text recognition is a long-standing research topic in the field of pattern recognition.
no code implementations • 25 Feb 2019 • Xianfeng Tang, Boqing Gong, Yanwei Yu, Huaxiu Yao, Yandong Li, Haiyong Xie, Xiaoyu Wang
In this paper, we propose a novel framework for the citywide traffic volume inference using both dense GPS trajectories and incomplete trajectories captured by camera surveillance systems.