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Wei Li - IEEE Xplore Author Profile
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Wei Li

Publications
16
Citations
333
Publications by Year
20182025
Co-Authors:
Show All Co-Authors (62)
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Wei Li

Affiliation

College of Computer Science and Technology
Harbin Engineering University
Harbin, China
Modeling and Emulation in E-Government National Engineering Laboratory
Harbin Engineering University
Harbin, China

Publication Topics

Biography

Wei Li received the PhD degree from the College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, China. He is the staff engineer at Inceptio and the postdoctoral researcher at Peking University, China. His research interests include robotics, computer vision and computer graphics. He has published more than 10 papers in top journals and conferences including the Science Robotics, SIGGRAPH, the IEEE Transactions on Visualization and Computer Graphics, AAAI, CVPR, and ICRA.(Based on document published on 26 July 2021).
Publications
16
Citations
333
Publications by Year
20182025
Co-Authors:
Show All Co-Authors (62)
Author's Published Works

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Self-Supervised Uncertainty-Guided Refinement for Robust Joint Optical Flow and Depth Estimation

Rokia Abdein;Wei Li;Chenghao Li;Xiangping Zheng;Rahul Yadav

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Jointly estimating the optical flow and depth tasks in real-world scenes presents considerable hurdles due to some phenomena, such as occlusion, ambiguous textures, and illumination variation. The lack of guidance from the labeled data makes these challenges harder to overcome. This paper presents a novel approach to learning the regions with high uncertainties in a self-supervised manner. Our met...Show More

Self-Supervised Uncertainty-Guided Refinement for Robust Joint Optical Flow and Depth Estimation

Rokia Abdein;Wei Li;Chenghao Li;Xiangping Zheng;Rahul Yadav

Year: 2025 | Conference Paper |

Dynamic Graph Convolutional Networks with Spatiotemporal Missing Pattern Awareness

Bingheng Pang;Zhuoxuan Liang;Wei Li;Xiangping Zheng;Rokia Abdein

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Missing data is ubiquitous phenomenon in the time series community, significantly challenging forecasting due to incomplete ground truth and sparse data. Most previous Multi-variate Time Series Forecasting with Missing Values (MTSFMV) approaches usually assume static missing patterns, neglecting the dynamic changes over time and space, leading to suboptimal forecasting results. To tackle these cha...Show More

Dynamic Graph Convolutional Networks with Spatiotemporal Missing Pattern Awareness

Bingheng Pang;Zhuoxuan Liang;Wei Li;Xiangping Zheng;Rokia Abdein

Year: 2025 | Conference Paper |

Brain Waves Unleashed: Illuminating Neonatal Seizure Detection via Multi-scale Hierarchical Modeling

Bingheng Pang;Zhuoxuan Liang;Wei Li;Xiangxu Meng;Chenhao Wang;Yilin Ren

2024 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2024 | Conference Paper |
Neonatal seizures are a prevalent clinical manifestation of neurological disorders and can potentially impact the neurodevelopment of the infant’s brain. Accurate and timely detection of neonatal epilepsy is crucial for early diagnosis and treatment. However, because of the complexity of newborn brains and signal instability, current seizure detection methods often produce false positives and nega...Show More

Brain Waves Unleashed: Illuminating Neonatal Seizure Detection via Multi-scale Hierarchical Modeling

Bingheng Pang;Zhuoxuan Liang;Wei Li;Xiangxu Meng;Chenhao Wang;Yilin Ren

Year: 2024 | Conference Paper |

A Network Latency Prediction Method Based on Sequence Decomposition and GNN

Chuhao Chen;Wei Li;Xiangxu Meng;Linying Yang;Wenqi Zheng

2024 International Joint Conference on Neural Networks (IJCNN)
Year: 2024 | Conference Paper |
Ultra reliable low latency communication (URLLC) has emerged as a crucial element in various communication services due to its ability to provide highly dependable and real-time connections. However, existing time series models utilized in URLLC scenarios often overlook the interconnections among delay cycles, trends, and bursts. Therefore, they fail to effectively capture the temporal periodic st...Show More

A Network Latency Prediction Method Based on Sequence Decomposition and GNN

Chuhao Chen;Wei Li;Xiangxu Meng;Linying Yang;Wenqi Zheng

Year: 2024 | Conference Paper |

Semi-Supervised Contrastive Learning for Few-Shot Indoor Positioning via 5G NR

Wenqi Zheng;Linying Yang;Wei Li;Xiangxu Meng;Jianing Chen;Zhihong Wang

2024 International Joint Conference on Neural Networks (IJCNN)
Year: 2024 | Conference Paper |
Leveraging the enhanced bandwidth and the advantages conferred by Massive Multiple-Input Multiple-Output (MIMO) technology, 5G New Radio (NR) extends unprecedented prospects for high-fidelity indoor positioning systems. However, the expensive equipment required for 5G CSI collection and the complex workforce involved often lead to a scarcity of "CSI-position" samples, hindering the deployment of d...Show More

Semi-Supervised Contrastive Learning for Few-Shot Indoor Positioning via 5G NR

Wenqi Zheng;Linying Yang;Wei Li;Xiangxu Meng;Jianing Chen;Zhihong Wang

Year: 2024 | Conference Paper |

Low-Complexity and Efficient Dependent Subtask Offloading Strategy in IoT Integrated With Multi-Access Edge Computing

Haiyan Wang;Wei Li;Jiayu Sun;Li Zhao;Xingmei Wang;Hongwu Lv;Guangsheng Feng

IEEE Transactions on Network and Service Management
Year: 2024 | Volume: 21, Issue: 1 | Journal Article |
Cited by: Papers (11)
Multi-access edge computing (MEC) has been booming in recent years, as it promises to fulfill the growing low-latency requirements of applications on large amounts of Internet of Things (IoT) devices. Nevertheless, as latency-sensitive applications tend to become more complicated, existing schemes are too sophisticated, which may result in exceeding the real-time requirements of IoT systems. In th...Show More

Low-Complexity and Efficient Dependent Subtask Offloading Strategy in IoT Integrated With Multi-Access Edge Computing

Haiyan Wang;Wei Li;Jiayu Sun;Li Zhao;Xingmei Wang;Hongwu Lv;Guangsheng Feng

Year: 2024 | Volume: 21, Issue: 1 | Journal Article |

LoT: A Transformer-Based Approach Based on Channel State Information for Indoor Localization

Wei Li;Xiangxu Meng;Zheng Zhao;Zhihan Liu;Chuhao Chen;Huiqiang Wang

IEEE Sensors Journal
Year: 2023 | Volume: 23, Issue: 22 | Journal Article |
Cited by: Papers (11)
Fingerprint-based localization has gained significant commercial value in recent years and have brought new opportunities to location-based services in indoor environments. Existing fingerprint indoor positioning studies have focused on various neural networks (e.g., convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid approaches). However, these methods are limited b...Show More

LoT: A Transformer-Based Approach Based on Channel State Information for Indoor Localization

Wei Li;Xiangxu Meng;Zheng Zhao;Zhihan Liu;Chuhao Chen;Huiqiang Wang

Year: 2023 | Volume: 23, Issue: 22 | Journal Article |

GANet: Goal Area Network for Motion Forecasting

Mingkun Wang;Xinge Zhu;Changqian Yu;Wei Li;Yuexin Ma;Ruochun Jin;Xiaoguang Ren;Dongchun Ren;Mingxu Wang;Wenjing Yang

2023 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2023 | Conference Paper |
Cited by: Papers (40)
Predicting the future motion of road participants is crucial for autonomous driving but is extremely challenging due to staggering motion uncertainty. Recently, most motion forecasting methods resort to the goal-based strategy, i.e., predicting endpoints of motion trajectories as conditions to regress the entire trajectories, so that the search space of solution can be reduced. However, accurate g...Show More

GANet: Goal Area Network for Motion Forecasting

Mingkun Wang;Xinge Zhu;Changqian Yu;Wei Li;Yuexin Ma;Ruochun Jin;Xiaoguang Ren;Dongchun Ren;Mingxu Wang;Wenjing Yang

Year: 2023 | Conference Paper |

Bat: Bi-Alignment Based On Transformation in Multi-Target Domain Adaptation for Semantic Segmentation

Xian Zhong;Wei Li;Liang Liao;Jing Xiao;Wenxuan Liu;Wenxin Huang;Zheng Wang

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
While enlightening progress has been made recently in single-target domain adaptive semantic segmentation (ST-DASS), the multi-peak distributed multi-target domain cannot be directly aligned well with the single-peak distributed source domain. As a result, it is impossible for existing methods to handle the more realistic multi-target domain adaptive semantic segmentation (MT-DASS) tasks. To solve...Show More

Bat: Bi-Alignment Based On Transformation in Multi-Target Domain Adaptation for Semantic Segmentation

Xian Zhong;Wei Li;Liang Liao;Jing Xiao;Wenxuan Liu;Wenxin Huang;Zheng Wang

Year: 2023 | Conference Paper |

A Lightweight and Detector-Free 3D Single Object Tracker on Point Clouds

Yan Xia;Qiangqiang Wu;Wei Li;Antoni B. Chan;Uwe Stilla

IEEE Transactions on Intelligent Transportation Systems
Year: 2023 | Volume: 24, Issue: 5 | Journal Article |
Cited by: Papers (17)
Recent works on 3D single object tracking treat the task as a target-specific 3D detection task, where an off-the-shelf 3D detector is commonly employed for the tracking. However, it is non-trivial to perform accurate target-specific detection since the point cloud of objects in raw LiDAR scans is usually sparse and incomplete. In this paper, we address this issue by explicitly leveraging temporal...Show More

A Lightweight and Detector-Free 3D Single Object Tracker on Point Clouds

Yan Xia;Qiangqiang Wu;Wei Li;Antoni B. Chan;Uwe Stilla

Year: 2023 | Volume: 24, Issue: 5 | Journal Article |

Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-Based Perception

Xinge Zhu;Hui Zhou;Tai Wang;Fangzhou Hong;Wei Li;Yuexin Ma;Hongsheng Li;Ruigang Yang;Dahua Lin

IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2022 | Volume: 44, Issue: 10 | Journal Article |
Cited by: Papers (60)
State-of-the-art methods for driving-scene LiDAR-based perception (including point cloud semantic segmentation, panoptic segmentation and 3D detection, etc.) often project the point clouds to 2D space and then process them via 2D convolution. Although this cooperation shows the competitiveness in the point cloud, it inevitably alters and abandons the 3D topology and geometric relations. A natural ...Show More

Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-Based Perception

Xinge Zhu;Hui Zhou;Tai Wang;Fangzhou Hong;Wei Li;Yuexin Ma;Hongsheng Li;Ruigang Yang;Dahua Lin

Year: 2022 | Volume: 44, Issue: 10 | Journal Article |

Semantic Concentration for Domain Adaptation

Shuang Li;Mixue Xie;Fangrui Lv;Chi Harold Liu;Jian Liang;Chen Qin;Wei Li

2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2021 | Conference Paper |
Cited by: Papers (64)
Domain adaptation (DA) paves the way for label annotation and dataset bias issues by the knowledge transfer from a label-rich source domain to a related but unlabeled target domain. A mainstream of DA methods is to align the feature distributions of the two domains. However, the majority of them focus on the entire image features where irrelevant semantic information, e.g., the messy background, i...Show More

Semantic Concentration for Domain Adaptation

Shuang Li;Mixue Xie;Fangrui Lv;Chi Harold Liu;Jian Liang;Chen Qin;Wei Li

Year: 2021 | Conference Paper |

Dynamic Domain Adaptation for Efficient Inference

Shuang Li;JinMing Zhang;Wenxuan Ma;Chi Harold Liu;Wei Li

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2021 | Conference Paper |
Cited by: Papers (9)
Domain adaptation (DA) enables knowledge transfer from a labeled source domain to an unlabeled target domain by reducing the cross-domain distribution discrepancy. Most prior DA approaches leverage complicated and powerful deep neural networks to improve the adaptation capacity and have shown remarkable success. However, they may have a lack of applicability to real-world situations such as real-t...Show More

Dynamic Domain Adaptation for Efficient Inference

Shuang Li;JinMing Zhang;Wenxuan Ma;Chi Harold Liu;Wei Li

Year: 2021 | Conference Paper |

Transferable Semantic Augmentation for Domain Adaptation

Shuang Li;Mixue Xie;Kaixiong Gong;Chi Harold Liu;Yulin Wang;Wei Li

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2021 | Conference Paper |
Cited by: Papers (98)
Domain adaptation has been widely explored by transferring the knowledge from a label-rich source domain to a related but unlabeled target domain. Most existing domain adaptation algorithms attend to adapting feature representations across two domains with the guidance of a shared source-supervised classifier. However, such classifier limits the generalization ability towards unlabeled target reco...Show More

Transferable Semantic Augmentation for Domain Adaptation

Shuang Li;Mixue Xie;Kaixiong Gong;Chi Harold Liu;Yulin Wang;Wei Li

Year: 2021 | Conference Paper |

Fast Texture Mapping Adjustment via Local/Global Optimization

Wei Li;Huajun Gong;Ruigang Yang

IEEE Transactions on Visualization and Computer Graphics
Year: 2019 | Volume: 25, Issue: 6 | Journal Article |
Cited by: Papers (20)
This paper deals with the texture mapping of a triangular mesh model given a set of calibrated images. Different from the traditional approach of applying projective texture mapping with model parameterizations, we develop an image-space texture optimization scheme that aims to reduce visible seams or misalignment at texture or depth boundaries. Our novel scheme starts with an efficient local (and...Show More

Fast Texture Mapping Adjustment via Local/Global Optimization

Wei Li;Huajun Gong;Ruigang Yang

Year: 2019 | Volume: 25, Issue: 6 | Journal Article |

Robust Surface Light Field Modeling

Wei Li;Hui Qiao;Chen Zhao;Zhongqin Wu;Ruigang Yang

2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)
Year: 2018 | Conference Paper |
Cited by: Papers (2)
Surface light field advances conventional light field rendering techniques by utilizing geometry information. Using surface light field, real-world objects with complex appearance could be faithfully represented. This capability could play an important role in many VR/AR applications. However, an accurate geometric model is needed for surface light field sampling and processing, which limits its w...Show More

Robust Surface Light Field Modeling

Wei Li;Hui Qiao;Chen Zhao;Zhongqin Wu;Ruigang Yang

Year: 2018 | Conference Paper |

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