Qiang Li
Associate Director
SenseTime, Beijing, China
E-mail: leetsiang.cloud@gmail.com
[Google Scholar] [GitHub]
Brief Bio:
I am an Associate Director of R&D at SenseTime, working with Chen Qian. Before that, I was an Algorithm Engineer at Y-tech (formerly Y-lab), Kuaishou Technology, from May 2018 to Feb. 2023. I was an Algorithm Intern at Didi Research Institute, Didi Chuxing, from Oct. 2017 to May 2018.
I obtained the PhD degree from University of Technology Sydney (UTS) supervised by Prof. Dacheng Tao and the joint PhD degree from The Hong Kong Polytechnic University (PolyU) co-supervised by Prof. Jane You, both in 2018.
I received the BEng degree in Electronics and Information Engineering, and MEng degree in Signals and Information Processing, both from Huazhong University of Science and Technology (HUST), Wuhan, China, in 2010 and 2013, respectively.
My primary research involves deep learning, machine learning and probabilistic graphical models, with particular interests in deep generative models, representation learning and structured prediction. With these tools, my research also covers applications in computer vision, image processing and data mining.
** Job positions are open for researchers and interns at SenseTime! **
Selected Conference Publications:
FEditNet: few-shot editing of latent semantics in GAN spaces [PDF]
Mengfei Xia, Yezhi Shu, Yuji Wang, Yu-Kun Lai, Qiang Li, Pengfei Wan, Zhongyuan Wang, and Yong-Jin Liu
AAAI Conference on Artificial Intelligence (AAAI), Feb. 2023. (oral)
Exploring Set Similarity for Dense Self-supervised Representation Learning [PDF]
Zhaoqing Wang, Qiang Li^, Guoxin Zhang, Pengfei Wan, Wen Zheng, Nannan Wang, Mingming Gong, Tongliang Liu
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2022. (^corresponding author)
Adversarial Training Methods for Network Embedding [PDF]
Quanyu Dai, Xiao Shen, Liang Zhang, Qiang Li, Dan Wang
The World Wide Web Conference (WWW), May 2019, pp. 329-339. (oral)
Large-Scale Order Dispatch in On-Demand Ride-Sharing Platforms: A Learning and Planning Approach [PDF] [Talk]
Zhe Xu, Zhixin Li, Qingwen Guan, Dingshui Zhang, Qiang Li, Junxiao Nan, Chunyang Liu, Wei Bian and Jieping Ye
SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Aug. 2018, pp. 905--913. (oral)
Improving Stochastic Block Models by Incorporating Power-Law Degree Characteristic [PDF]
Maoying Qiao, Jun Yu, Wei Bian, Qiang Li, Dacheng Tao
International Joint Conference on Artificial Intelligence (IJCAI), Aug. 2017, pp. 2620--2626.
Selected Journal Publications:
Pyramid Geometric Consistency Learning For Semantic Segmentation
Xian Zhang, Qiang Li^, Zhibin Quan, Wankou Yang^
Pattern Recognition (PR), 2023. (^corresponding author)
Preprints:
Bridging CLIP and StyleGAN through Latent Alignment for Image Editing [PDF]
Wanfeng Zheng, Qiang Li^, Xiaoyan Guo, Pengfei Wan, Zhongyuan Wang
arXiv preprint arXiv:2210.04506. (^corresponding author)
ITTR: Unpaired Image-to-Image Translation with Transformers [PDF]
Wanfeng Zheng, Qiang Li^, Guoxin Zhang, Pengfei Wan, Zhongyuan Wang
arXiv preprint arXiv:2203.16015. (^corresponding author)
StyleRemix: An Interpretable Representation for Neural Image Style Transfer [PDF]
Hongmin Xu*, Qiang Li*, Wenbo Zhang, Wen Zheng
arXiv preprint arXiv:1902.10425. (*co-first author)
Reading Notes:
Convex Optimization [PDF]. Reference: Stephen Boyd and Lieven Vandenberghe's book on convex optimization.
Variational Inference [PDF]. Reference: Martin Wainwright and Michael Jordan's book on graphical models, exponential families and variational inference.
Sparse and Kernel Methods [PDF]. Reference: Christopher Bishop's PRML book and representative papers.
Mixture of Trees [PDF]. Reference: several classical and recent papers.
Determinantal Point Process [PDF]. Reference: Alex Kulesza and Ben Taskar's tutorial on DPP for machine learning.