柯炜 副教授,博士生导师
入选陕西省高层次人才青年项目
入选西安交通大学青年拔尖人才支持计划
入选西安交通大学“思源学者”引才计划
入选西安交通大学“小米学者”
研究兴趣为计算机视觉与机器学习。
入选陕西省高层次人才青年项目
入选西安交通大学青年拔尖人才支持计划
入选西安交通大学“思源学者”引才计划
入选西安交通大学“小米学者”
研究兴趣为计算机视觉与机器学习。
近三年没有博士生指标,谢谢大家厚爱!
每年可招收计算机科学与技术学术型硕士和软件工程专业型硕士。不定期会招收计算机科学与技术专业博士。
有意者请邮件联系:
wei.ke[AT] mail.xjtu.edu.cn
I'm sorry that there is no position for international students right now in my team.
我的院外合作导师每年各自有1个统考指标,欢迎大家联系他们。具体信息如下
李吉亮 教授,网安空间安全学院,jiliang.li[at]xjtu.edu.cn,研究方向:AI安全
许翔宇 教授,数学学院,xuxiangyu2014[at]gmail.com,研究方向:计算机视觉
王治国 教授,数学学院,emailwzg[at]mail.xjtu.edu.cn,研究方向:地学大数据
西安交通大学(曲江校区)西四楼
西部科技创新港四号巨构
Email: wei.ke[at]mail.xjtu.edu.cn
主持项目:
W. Ke, J. Chen, J. Jiao, G. Zhao, and Q. Ye, “SRN: Side-output Residual Network for Object Symmetry Detection”. CVPR Oral, 2017.
W. Ke, J. Chen and Q. Ye. “Deep contour and symmetry scored object proposal”. Pattern Recognition Letter”, 2019.
C. Liu+, W. Ke+, F. Qin, and Q. Ye, “Linear Span Network for Object Skeleton Detection ”, ECCV, 2018.
C. Liu, F. Wan, W. Ke, et al., “Orthogonal Decomposition Network For Pixel-wise Binary Classification”, CVPR, 2019.
C. Liu, W. Ke, J. Jiao, and Q. Ye. “RSRN: Rich Side-output Residual Network for Medial Axis Detection”. ICCV Workshop on Detecting Symmetry in the Wild (Oral), 2017.
C. Liu , D. Luo, Y. Zhang, W. Ke, et al., Parametric Skeleton Generation via Gaussian Mixture Models. CVPR Workshop on Deep Learning for Geometric Shape Understanding (Oral), 2019.
W. Ke, T. Zhang, Z. Huang, Q. Ye, etc., "Multiple Anchor Learning for Visual Object Detection". CVPR, 2020.
X. Chen, P. Wei, W. Ke, Q. Ye, and J. Jiao. “Pedestrian detection with deep convolutional neural network”. In: ACCV Workshops, pp. 354–365, 2014.
W. Ke and Y. Zhang et al. “Pedestrian detection via PCA filters based convolutional channel features”. In: ICASSP, pp. 1394–1398, 2015.
Q. Ye, T. Zhang, and W. Ke* et al. “Self-learning Scene-specific Pedestrian Detectors using a Progressive Latent Model”. In: CVPR2017.
Q. Ye, T. Zhang, and W. Ke*. "Progressive Latent Models for Self-learning Scene-specific Pedestrian Detectors". IEEE T-ITS, 2019.