no code implementations • 5 Mar 2024 • Yanchen Guan, Haicheng Liao, Zhenning Li, Jia Hu, Runze Yuan, Yunjian Li, Guohui Zhang, Chengzhong Xu
In the rapidly evolving landscape of autonomous driving, the capability to accurately predict future events and assess their implications is paramount for both safety and efficiency, critically aiding the decision-making process.
no code implementations • 13 Jul 2021 • Zhenning Li, Chengzhong Xu, Guohui Zhang
Inefficient traffic signal control methods may cause numerous problems, such as traffic congestion and waste of energy.
no code implementations • 20 Apr 2021 • Zhenning Li, Hao Yu, Guohui Zhang, Shangjia Dong, Cheng-Zhong Xu
Inefficient traffic control may cause numerous problems such as traffic congestion and energy waste.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 21 Feb 2019 • Zhihui Su, Ming Ye, Guohui Zhang, Lei Dai, Jianda Sheng
Features from different stages are aggregated to obtain abundant contextual information, leading to robustness to poses, partial occlusions and low resolution.
Ranked #3 on Pose Estimation on MPII Human Pose (using extra training data)
no code implementations • 17 May 2018 • Guohui Zhang, Gaoyuan Liang, Fang Su, Fanxin Qu, Jing-Yan Wang
We proposed to embed the attributes of dif-ferent domains by a shared convolutional neural network (CNN), learn a domain-independent CNN model to represent the information shared by dif-ferent domains by matching across domains, and a domain-specific CNN model to represent the information of each domain.
no code implementations • 2 Mar 2017 • Yanyan Geng, Guohui Zhang, Weizhi Li, Yi Gu, Ru-Ze Liang, Gaoyuan Liang, Jingbin Wang, Yanbin Wu, Nitin Patil, Jing-Yan Wang
In this paper, we study the problem of image tag complete and proposed a novel method for this problem based on a popular image representation method, convolutional neural network (CNN).