Long Bao - IEEE Xplore Author Profile

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Author details

Long Bao

Publications

19

Citations

351

Publications by Year

20122024

Co-Authors:

Abdelrahman AbdelhamedMahmoud AfifiSos AgaianYuang AiDongwoon Bai

Show All Co-Authors (269)

Long Bao

Affiliation

Camera Department

Video Algorithm Group

Xiaomi Inc., China

Publication Topics

Convolutional Neural Network,
L1 Loss,
Patch Size,
AdamW Optimizer,
Attention Module,
Clear Image,
Data Augmentation,
Fine-tuning Stage,
Frequency Sub-bands,
Image Sensor,
Low-resolution Images,
Noise Model

Biography

Long Bao (S’11) received the B.S. and M.S. degrees in electrical engineering from Hunan University, Changsha, China, in 2011 and 2014, respectively. He is currently pursuing the Ph.D. degree in electrical engineering with Tufts University, Medford, MA, USA. He was a Research Assistant with the Department of Computer and Information Science at University of Macau from 2012 to 2014. His research interests are machine learning, image quality assessment, image denoising, image coloring, and multimedia security. He is a Student Member of the IEEE-Eta Kappa Nu.(Based on document published on 10 August 2017).

Publications

19

Citations

351

Publications by Year

20122024

Co-Authors:

Abdelrahman Abdelhamed
Mahmoud Afifi
Sos Agaian
Yuang Ai
Dongwoon Bai

Show All Co-Authors (269)

Author's Published Works

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Showing 1-6 of 6 results

Conferences (3)

Journals (3)

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Yiying Yang(6)
Lin Zhang(3)
Xinhang Li(3)
Chen Xu(3)
Qinwen Wang(3)
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China(2)
School of Artificial Intelligence, Beijing University of Posts and Telecommunications (BUPT), Beijing, China(1)
Academy for Engineering and Technology, Fudan University, Shanghai, China(1)
Beijing Big Data Center, Beijing, China(1)
School of Software, University of South, China(1)
2022 18th International Conference on Mobility, Sensing and Networking (MSN)(2)
2024 IEEE/CIC International Conference on Communications in China (ICCC Workshops)(1)
IEEE Robotics and Automation Letters(1)
IEEE Transactions on Instrumentation and Measurement(1)
IEEE Transactions on Intelligent Transportation Systems(1)
IEEE(6)
Guangzhou, China(2)
Hangzhou, China(1)
Deep Q-network(3)
Multi-agent Reinforcement Learning(3)
Urban Scenes(3)
Ablation Experiments(2)
Average Reward(2)

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Results

Unbounded-GS: Extending 3D Gaussian Splatting With Hybrid Representation for Unbounded Large-Scale Scene Reconstruction

Wanzhang Li;Fukun Yin;Wen Liu;Yiying Yang;Xin Chen;Biao Jiang;Gang Yu;Jiayuan Fan

IEEE Robotics and Automation Letters

Year: 2024 | Volume: 9, Issue: 12 | Journal Article |

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Modeling large-scale scenes from multi-view images is challenging due to the trade-off dilemma between visual quality and computational cost. Existing NeRF-based methods have made advancements in neural implicit representation through volumetric ray-marching, but still struggle to deal with cubically growing sampling space in large-scale scenes. Fortunately, the rendering approach based on 3D Gaus...Show More

Unbounded-GS: Extending 3D Gaussian Splatting With Hybrid Representation for Unbounded Large-Scale Scene Reconstruction

Wanzhang Li;Fukun Yin;Wen Liu;Yiying Yang;Xin Chen;Biao Jiang;Gang Yu;Jiayuan Fan

IEEE Robotics and Automation Letters

Year: 2024 | Volume: 9, Issue: 12 | Journal Article |

Enabling Trustable Financing: A Verifiable Privacy-Preserving Cross-Chain Protocol

Lidu Lou;Wenbin He;Xiangyun Tang;Yiying Yang;Tao Zhang;Yanyu Cheng;Cai Xu

2024 IEEE/CIC International Conference on Communications in China (ICCC Workshops)

Year: 2024 | Conference Paper |

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Generative AI-enabled wireless communications enhance efficiency and offer new tools for supply chain finance (SCF) innovation. SCF optimizes capital efficiency and reduces risk, but information asymmetry hinders risk assessment. Blockchain technology addresses these challenges by solving information asymmetry and regulatory issues. However, issues of privacy protection, reliability, and traceabil...Show More

Enabling Trustable Financing: A Verifiable Privacy-Preserving Cross-Chain Protocol

Lidu Lou;Wenbin He;Xiangyun Tang;Yiying Yang;Tao Zhang;Yanyu Cheng;Cai Xu

2024 IEEE/CIC International Conference on Communications in China (ICCC Workshops)

Year: 2024 | Conference Paper |

Progression Cognition Reinforcement Learning With Prioritized Experience for Multi-Vehicle Pursuit

Xinhang Li;Yiying Yang;Zheng Yuan;Zhe Wang;Qinwen Wang;Chen Xu;Lei Li;Jianhua He;Lin Zhang

IEEE Transactions on Intelligent Transportation Systems

Year: 2024 | Volume: 25, Issue: 8 | Journal Article |

Cited by: Papers (2)

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Multi-vehicle pursuit (MVP) such as autonomous police vehicles pursuing suspects is important but very challenging due to its mission and safety-critical nature. While multi-agent reinforcement learning (MARL) algorithms have been proposed for MVP in structured grid-pattern roads, the existing algorithms use random training samples in centralized learning, which leads to homogeneous agents showing...Show More

Progression Cognition Reinforcement Learning With Prioritized Experience for Multi-Vehicle Pursuit

Xinhang Li;Yiying Yang;Zheng Yuan;Zhe Wang;Qinwen Wang;Chen Xu;Lei Li;Jianhua He;Lin Zhang

IEEE Transactions on Intelligent Transportation Systems

Year: 2024 | Volume: 25, Issue: 8 | Journal Article |

Improvement by Monte Carlo for Trajectory Similarity-Based RUL Prediction

Diwang Ruan;Lin Ma;Yiying Yang;Jianping Yan;Clemens Gühmann

IEEE Transactions on Instrumentation and Measurement

Year: 2024 | Volume: 73 | Journal Article |

Cited by: Papers (1)

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Trajectory similarity-based evaluation is the most intuitive method for remaining useful life (RUL) prediction when abundant run-to-failure data are available. However, practical scenarios often present a challenge with limited access to such trajectories. This article introduces an improved similarity-based method to bridge the gap by employing Monte Carlo to generate more trajectories. To begin,...Show More

Improvement by Monte Carlo for Trajectory Similarity-Based RUL Prediction

Diwang Ruan;Lin Ma;Yiying Yang;Jianping Yan;Clemens Gühmann

IEEE Transactions on Instrumentation and Measurement

Year: 2024 | Volume: 73 | Journal Article |

An Opponent-Aware Reinforcement Learning Method for Team-to-Team Multi-Vehicle Pursuit via Maximizing Mutual Information Indicator

Qinwen Wang;Xinhang Li;Zheng Yuan;Yiying Yang;Chen Xu;Lin Zhang

2022 18th International Conference on Mobility, Sensing and Networking (MSN)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

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The pursuit-evasion game in Smart City brings a profound impact on the Multi-vehicle Pursuit (MVP) problem, when police cars cooperatively pursue suspected vehicles. Existing studies on the MVP problems tend to set evading vehicles to move randomly or in a fixed prescribed route. The opponent modeling method has proven considerable promise in tackling the non-stationary caused by the adversary age...Show More

An Opponent-Aware Reinforcement Learning Method for Team-to-Team Multi-Vehicle Pursuit via Maximizing Mutual Information Indicator

Qinwen Wang;Xinhang Li;Zheng Yuan;Yiying Yang;Chen Xu;Lin Zhang

2022 18th International Conference on Mobility, Sensing and Networking (MSN)

Year: 2022 | Conference Paper |

Graded-Q Reinforcement Learning with Information-Enhanced State Encoder for Hierarchical Collaborative Multi-Vehicle Pursuit

Yiying Yang;Xinhang Li;Zheng Yuan;Qinwen Wang;Chen Xu;Lin Zhang

2022 18th International Conference on Mobility, Sensing and Networking (MSN)

Year: 2022 | Conference Paper |

Cited by: Papers (2)

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The multi-vehicle pursuit (MVP), as a problem abstracted from various real-world scenarios, is becoming a hot research topic in the Intelligent Transportation System (ITS). The combination of Artificial Intelligence (AI) and connected vehicles has greatly promoted the research development of MVP. However, existing works on MVP pay little attention to the importance of information exchange and coop...Show More

Graded-Q Reinforcement Learning with Information-Enhanced State Encoder for Hierarchical Collaborative Multi-Vehicle Pursuit

Yiying Yang;Xinhang Li;Zheng Yuan;Qinwen Wang;Chen Xu;Lin Zhang

2022 18th International Conference on Mobility, Sensing and Networking (MSN)

Year: 2022 | Conference Paper |

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