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Baoling Liu - IEEE Xplore Author Profile
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Baoling Liu

Publications
4
Citations
14
Publications by Year
20222024
Co-Authors:
Show All Co-Authors (9)
Image of author Baoling Liu

Baoling Liu

Affiliation

State Key Laboratory of Networking and Switching Technology
Beijing University of Posts and Telecommunications
Beijing, China

Publication Topics

Biography

Baoling Liu is currently working toward the M.Sc. degree in computer science with the South China University of Technology, Guangzhou, China.,Her research interests include machine learning and image processing.(Based on document published on 6 February 2024).
Publications
4
Citations
14
Publications by Year
20222024
Co-Authors:
Show All Co-Authors (9)
Author's Published Works

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Privacy Preserving Quantum Search Mechanism Using Grover's Algorithm

Keyi Ju;Xiaoqi Qin;Hui Zhong;Xinyue Zhang;Miao Pan;Baoling Liu

2024 International Conference on Quantum Communications, Networking, and Computing (QCNC)
Year: 2024 | Conference Paper |
Quantum computing changes the way of solving complex problems and handling vast datasets. The introduction of quantum search algorithms, notably Grover's algorithm, addresses the inefficiencies of classical search methods in handling vast datasets by offering substantial speed improvements. However, an inherent aspect of quantum searches is their failure probability. This paper aims to explore the...Show More

Privacy Preserving Quantum Search Mechanism Using Grover's Algorithm

Keyi Ju;Xiaoqi Qin;Hui Zhong;Xinyue Zhang;Miao Pan;Baoling Liu

Year: 2024 | Conference Paper |

Harnessing Inherent Noises for Privacy Preservation in Quantum Machine Learning

Keyi Ju;Xiaoqi Qin;Hui Zhong;Xinyue Zhang;Miao Pan;Baoling Liu

ICC 2024 - IEEE International Conference on Communications
Year: 2024 | Conference Paper |
Cited by: Papers (2)
Quantum computing revolutionizes the way of solving complex problems and handling vast datasets, which shows great potential to accelerate the machine learning process. However, data leakage in quantum machine learning (QML) may present privacy risks. Although differential privacy (DP), which protects privacy through the injection of artificial noise, is a well-established approach, its applicatio...Show More

Harnessing Inherent Noises for Privacy Preservation in Quantum Machine Learning

Keyi Ju;Xiaoqi Qin;Hui Zhong;Xinyue Zhang;Miao Pan;Baoling Liu

Year: 2024 | Conference Paper |

Text-Guided Portrait Image Matting

Yong Xu;Xin Yao;Baoling Liu;Yuhui Quan;Hui Ji

IEEE Transactions on Artificial Intelligence
Year: 2024 | Volume: 5, Issue: 8 | Journal Article |
Image matting is a technique used to separate the foreground of an image from the background, which estimates an alpha matte that indicates pixel-wise degree of transparency. To precisely extract target objects and address the ambiguity of solutions in image matting, many existing approaches employ a trimap or background image provided by the user as additional input to guide the matting process. ...Show More

Text-Guided Portrait Image Matting

Yong Xu;Xin Yao;Baoling Liu;Yuhui Quan;Hui Ji

Year: 2024 | Volume: 5, Issue: 8 | Journal Article |

Unsupervised Deep Background Matting Using Deep Matte Prior

Yong Xu;Baoling Liu;Yuhui Quan;Hui Ji

IEEE Transactions on Circuits and Systems for Video Technology
Year: 2022 | Volume: 32, Issue: 7 | Journal Article |
Cited by: Papers (12)
Background matting is a recently developed image matting approach, with applications to image and video editing. It refers to estimating both the alpha matte and foreground from a pair of images with and without foreground objects. Recent work has applied deep learning to background matting, with very promising performance achieved. However, existing deep models are supervised which require a larg...Show More

Unsupervised Deep Background Matting Using Deep Matte Prior

Yong Xu;Baoling Liu;Yuhui Quan;Hui Ji

Year: 2022 | Volume: 32, Issue: 7 | Journal Article |

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