Qiang Li's Homepage

Qiang Li

Associate Director

SenseTime, Beijing, China

E-mail: leetsiang.cloud@gmail.com

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:

LiftedCL: Lifting Contrastive Learning for Human-Centric Perception [PDF] [Project] [Talk] [Code]

Ziwei Chen, Qiang Li^, Xiaofeng Wang, Wankou Yang^

International Conference on Learning Representations (ICLR), May 2023. (^corresponding author)

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)

CRIS: CLIP-Driven Referring Image Segmentation [PDF] [Code]

Zhaoqing Wang*, Yu Lu*, Qiang Li*, Xunqiang Tao, Yandong Guo, Mingming Gong, Tongliang Liu

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2022. (*co-first author)

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)

BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation [PDF] [Project] [Demo] [Code] [Dataset]

Mingcong Liu, Qiang Li^, Zekui Qin, Guoxin Zhang, Pengfei Wan, Wen Zheng

Neural Information Processing Systems (NeurIPS), Dec. 2021. (^corresponding author)

End-to-end Hand Mesh Recovery from a Monocular RGB Image [PDF] [Code]

Xiong Zhang*, Qiang Li*, Mo Hong, Wenbo Zhang, Wen Zheng

IEEE International Conference on Computer Vision (ICCV), Oct. 2019, pp. 2354--2364. (*co-first 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)

Adversarial Network Embedding [PDF] [Code]

Quanyu Dai, Qiang Li, Jian Tang, Dan Wang

AAAI Conference on Artificial Intelligence (AAAI), Feb. 2018

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.

Conditional Graphical Lasso for Multi-label Image Classification [PDF] [Poster] [Code]

Qiang Li, Maoying Qiao, Wei Bian, and Dacheng Tao

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2016, pp. 2977--2986.

Random Mixed Field Model for Mixed-Attribute Data Restoration [PDF] [Slides] [Code]

Qiang Li, Wei Bian, Richard Yi Da Xu, Jane You and Dacheng Tao

AAAI Conference on Artificial Intelligence (AAAI), Feb. 2016, pp. 1244--1250. (oral)

Selected Journal Publications:

Pyramid Geometric Consistency Learning For Semantic Segmentation

Xian Zhang, Qiang Li^, Zhibin Quan, Wankou Yang^

Pattern Recognition (PR), 2023. (^corresponding author)

Correlated Logistic Model with Elastic Net Regularization for Multilabel Image Classification [PDF] [Code]

Qiang Li, Bo Xie, Jane You, Wei Bian, and Dacheng Tao

IEEE Trans. on Image Processing (T-IP), vol.25, no.8, pp.3801--3813, 2016.

Local Metric Learning for Exemplar-based Object Detection [PDF] [Slides]

Xinge You, Qiang Li, Dacheng Tao, Weihua Ou and Mingming Gong

IEEE Trans. on Circuits and Systems for Video Technology (T-CSVT), vol.24, no.8, pp.1265–1276, 2014.

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)

Siamese Keypoint Prediction Network for Visual Object Tracking [PDF] [Code]

Qiang Li, Zekui Qin, Wenbo Zhang, Wen Zheng

arXiv preprint arXiv:2006.04078.

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.

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