no code implementations • 6 Jul 2023 • Zhen Gao, Shicong Liu, Yu Su, Zhongxiang Li, Dezhi Zheng
Moreover, based on the acquired channel semantic, we further propose a knowledge-driven deep-unfolding multi-user beamformer, which is capable of achieving good spectral efficiency with robustness to imperfect CSI in outdoor XR scenarios.
1 code implementation • 27 Apr 2023 • Linwei Chen, Ying Fu, Kaixuan Wei, Dezhi Zheng, Felix Heide
Existing instance segmentation techniques are primarily tailored for high-visibility inputs, but their performance significantly deteriorates in extremely low-light environments.
no code implementations • ICCV 2023 • Kang Ma, Ying Fu, Dezhi Zheng, Yunjie Peng, Chunshui Cao, Yongzhen Huang
Gait recognition has emerged as a promising technique for the long-range retrieval of pedestrians, providing numerous advantages such as accurate identification in challenging conditions and non-intrusiveness, making it highly desirable for improving public safety and security.
no code implementations • CVPR 2023 • Kang Ma, Ying Fu, Dezhi Zheng, Chunshui Cao, Xuecai Hu, Yongzhen Huang
Specifically, we create a dynamic attention mechanism between the features of neighboring pixels that not only adaptively focuses on key regions but also generates more expressive local motion patterns.
no code implementations • 28 Dec 2022 • Liheng Bian, Haoze Song, Lintao Peng, Xuyang Chang, Xi Yang, Roarke Horstmeyer, Lin Ye, Tong Qin, Dezhi Zheng, Jun Zhang
Benefiting from its single-photon sensitivity, single-photon avalanche diode (SPAD) array has been widely applied in various fields such as fluorescence lifetime imaging and quantum computing.
no code implementations • 8 May 2022 • Yang Wang, Zhen Gao, Dezhi Zheng, Sheng Chen, Deniz Gündüz, H. Vincent Poor
It is anticipated that 6G wireless networks will accelerate the convergence of the physical and cyber worlds and enable a paradigm-shift in the way we deploy and exploit communication networks.
no code implementations • 18 Jan 2022 • Zhen Gao, Minghui Wu, Chun Hu, Feifei Gao, Guanghui Wen, Dezhi Zheng, Jun Zhang
To this end, by modeling the key transmission modules as an end-to-end (E2E) neural network, this paper proposes a data-driven deep learning (DL)-based unified hybrid beamforming framework for both the time division duplex (TDD) and frequency division duplex (FDD) systems with implicit channel state information (CSI).