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Jingjing Li - IEEE Xplore Author Profile
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Jingjing Li

Publications
80
Citations
2,962
Publications by Year
20172025
Co-Authors:
Show All Co-Authors (101)
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Jingjing Li

Affiliation

School of Computer Science and Engineering
University of Electronic Science and Technology of China
Chengdu, China

Publication Topics

Biography

Jingjing Li received the M.Sc. and Ph.D. degrees in computer science from the University of Electronic Science and Technology of China, in 2013 and 2017, respectively, where he is currently a Research Fellow with the National Postdoctoral Program for Innovative Talents, School of Computer Science and Engineering. His research interests include machine learning, especially transfer learning, subspace learning, and recommender systems.(Based on document published on 2 September 2019).
Publications
80
Citations
2,962
Publications by Year
20172025
Co-Authors:
Show All Co-Authors (101)
Author's Published Works

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Geometry Compression Artifact Removal for V-PCC over a Wide Bitrate Range

Jian Xiong;Junhao Wu;Wang Luo;Jiucheng Xie;Hao Gao

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2024 | Conference Paper |
In video-based point cloud compression (V-PCC), point clouds are generated as videos via patch projection to be compressed using video coding techniques. However, a large number of filled empty pixels in the videos creates a fake context, which reduces the noise prediction accuracy in compression artifact removal. Moreover, mean square error (MSE)-based trained models perform better on low-bitrate...Show More

Geometry Compression Artifact Removal for V-PCC over a Wide Bitrate Range

Jian Xiong;Junhao Wu;Wang Luo;Jiucheng Xie;Hao Gao

Year: 2024 | Conference Paper |

Scene Flow Estimation from Point Clouds with Contrastive Loss and Dual Pseudo Labels

Mingliang Zhai;Kang Ni;Jiucheng Xie;Xuezhi Xiang;Hao Gao

2023 IEEE International Conference on Image Processing (ICIP)
Year: 2023 | Conference Paper |
Scene flow estimation aims to extract the 3D motion vector between each surface point in two consecutive point clouds. Pseudo-label-based approaches usually exploit point-to-point relations and 3D geometry information to generate the pseudo label for self-supervised learning. However, unreasonable results are still obtained due to the unexploited information of negative samples. Moreover, previous...Show More

Scene Flow Estimation from Point Clouds with Contrastive Loss and Dual Pseudo Labels

Mingliang Zhai;Kang Ni;Jiucheng Xie;Xuezhi Xiang;Hao Gao

Year: 2023 | Conference Paper |

Spike-Based Optical Flow Estimation Via Contrastive Learning

Mingliang Zhai;Kang Ni;Jiucheng Xie;Hao Gao

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
Spiking cameras have shown promising advantages for optical flow estimation in high-speed scenarios. The recent work SCFlow [1] attempts to train an optical flow model using spike frames based on a multi-scale flow reconstruction loss. However, only using the flow reconstruction loss is unable to effectively deal with the details of motion, which may lead to noise and blur in the estimated flow fi...Show More

Spike-Based Optical Flow Estimation Via Contrastive Learning

Mingliang Zhai;Kang Ni;Jiucheng Xie;Hao Gao

Year: 2023 | Conference Paper |

Cross-Modal Optical Flow Estimation via Modality Compensation and Alignment

Mingliang Zhai;Kang Ni;Jiucheng Xie;Hao Gao

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2023 | Conference Paper |
Cross-modal optical flow estimation aims to predict motion fields between two frames collected from different modalities, recently attracting intensive attention. However, a substantial yet challenging problem is how to match images across a large modal discrepancy. In this paper, we propose a modality compensation module (MCM) to extract complementary features from different modalities adaptively...Show More

Cross-Modal Optical Flow Estimation via Modality Compensation and Alignment

Mingliang Zhai;Kang Ni;Jiucheng Xie;Hao Gao

Year: 2023 | Conference Paper |

Learning Scene Flow from 3d Point Clouds with Cross-Transformer and Global Motion Cues

Mingliang Zhai;Kang Ni;Jiucheng Xie;Hao Gao

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Scene flow estimation is critical for real-world vision problems such as autonomous driving and augmented reality. Due to the popularity of 3D LiDAR sensors, scene flow estimation from 3D point clouds arouses increasing attention. Existing methods usually use a flow embedding-based layer to find correspondences between point pairs. However, only using a flow embedding-based layer is not enough to ...Show More

Learning Scene Flow from 3d Point Clouds with Cross-Transformer and Global Motion Cues

Mingliang Zhai;Kang Ni;Jiucheng Xie;Hao Gao

Year: 2023 | Conference Paper |

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