Name: Liu, Yang

Prefered Title: Dr.

Contact Information:

 +86 138 1180 2694

 This email address is being protected from spambots. You need JavaScript enabled to view it.

 

 

 

 

 

 

 

 

 

Liu Yang has received his doctor's degree (Computer Software and Theory) from Beihang University in 2019, after 8 years study and research in the State Key Laboratory of Software Development Environment.

    Between 2005 and 2009, he studied in Beihang University as an undergraduate. After graduation, he studied Mobile Computing and Mobile Network Application under the supervision of Prof. Niu Jianwei  as a graduate student. In 2011, he was accepted by Prof. Li Wei and joined the SKLSDE as a doctoral candidate.

    In the SKLSDE, his research was focused on Computer Vision and Pattern Recognition, especially image content understanding, and in 2016 he started the study of deep learning techniques. 

 

Publications:

[1] 牛建伟*,刘洋,卢邦辉,宋文芳。一种基于Wi-Fi信号指纹的楼宇内定位算法[J]。计算机研究与发展,2013年第3期,568-577页

[2] Niu Jianwei*, Liu Yang, Lin Jialiu, Zhu Like, Wang Kongqiao. Stroke++: A new Chinese input method for touch screen mobile phones[J]. International Journal of Human - Computer Studies, Elsevier, 2014, Pages 440-450

[3] Huang Lei*, Liu Yang, Wang Xindong, Liu Xianglong, Lang Bo. Graph-based active Semi-supervised learning a new perspective for relieving multi-class annotation labor[C]. IEEE ICME, 2014

[4] Liu Yang, Huang Lei, Liu Xianglong*, Lang Bo. A novel rotation adaptive object detection method based on pair Hough model[J]. Neurocomputing, Volume 194, 19 June 2016, Pages 246-259  [PDF]

[5] Liu Yang*, Huang Lei, Wang Siqi, Liu Xianglong, Lang Bo. Efficient Segmentation for Region-based Image Retrieval Using Edge Integrated Minimum Spanning Tree[C]. ICPR, 2016 [PDF]

[6] Wu Bo*, Liu Yang, Lang Bo, Huang Lei. DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model. Neurocomputing, 2018, Online Public

[7] Liu Yang*, Wu Bo, Lang Bo. Deep Salient Object Detection with Fuzzy Superpixel Extraction and Controlled Filter Convolution[C]. IJCNN, 2019 (Best Paper) [PDF][Source]