no code implementations • 3 May 2024 • Peijin Jia, Tuopu Wen, Ziang Luo, Mengmeng Yang, Kun Jiang, Zhiquan Lei, Xuewei Tang, Ziyuan Liu, Le Cui, Kehua Sheng, Bo Zhang, Diange Yang
Constructing high-definition (HD) maps is a crucial requirement for enabling autonomous driving.
no code implementations • 15 Apr 2024 • Mengmeng Yang, Ming Ding, Youyang Qu, Wei Ni, David Smith, Thierry Rakotoarivelo
The worldwide adoption of machine learning (ML) and deep learning models, particularly in critical sectors, such as healthcare and finance, presents substantial challenges in maintaining individual privacy and fairness.
no code implementations • 7 May 2023 • Jinyu Miao, Kun Jiang, Yunlong Wang, Tuopu Wen, Zhongyang Xiao, Zheng Fu, Mengmeng Yang, Maolin Liu, Diange Yang
High-precision vehicle localization with commercial setups is a crucial technique for high-level autonomous driving tasks.
no code implementations • 2 Mar 2023 • Yining Shi, Kun Jiang, Jiusi Li, Junze Wen, Zelin Qian, Mengmeng Yang, Ke Wang, Diange Yang
Grid-centric perception is a crucial field for mobile robot perception and navigation.
no code implementations • 25 Aug 2022 • Taohua Zhou, Yining Shi, Junjie Chen, Kun Jiang, Mengmeng Yang, Diange Yang
A novel method that realizes the feature-level fusion under the bird's-eye view (BEV) for a better feature representation is proposed.
2 code implementations • AAAI2022 2022 • Yun Xiao, Mengmeng Yang, Chenglong Li, Lei Liu, Jin Tang
RGBT tracking usually suffers from various challenging factors of fast motion, scale variation, illumination variation, thermal crossover and occlusion, to name a few.
Ranked #5 on Rgb-T Tracking on GTOT
no code implementations • 9 Aug 2020 • Mengmeng Yang, Lingjuan Lyu, Jun Zhao, Tianqing Zhu, Kwok-Yan Lam
Local differential privacy (LDP), as a strong privacy tool, has been widely deployed in the real world in recent years.
Cryptography and Security
no code implementations • 19 Apr 2020 • Yang Zhao, Jun Zhao, Mengmeng Yang, Teng Wang, Ning Wang, Lingjuan Lyu, Dusit Niyato, Kwok-Yan Lam
To avoid the privacy threat and reduce the communication cost, in this paper, we propose to integrate federated learning and local differential privacy (LDP) to facilitate the crowdsourcing applications to achieve the machine learning model.