Wenhan Luo

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I am an associate professor in Sun Yat-sen University, where I am conducting research on trustworthy AI and creative AI. Before being a faculty member in university, I worked as an applied research scientist at Tencent, solving real-world problems using computer vision and machine learning techniques. Prior to Tencent, I worked for Amazon (A9) in Palo Alto, California, where I developed deep models for better visual search experience. Before that, I worked as a research scientist in Tencent AI Lab. The techniques I have developed/involved have been shipped to several products in Tencent such as WeChat, Tencent Video, and myapp. I received the Ph.D. degree from Imperial College London, UK, 2016, M.E. degree from Institute of Automation, Chinese Academy of Sciences, China, 2012 and B.E. degree from Huazhong University of Science and Technology, China, 2009. [CV]


I am interested in several topics in computer vision and machine learning. Specifically, my current research focuses on trustworthy AI and creative AI, such as adversarial learning, image/video synthesis.


2022/08 - Paper accepted by WACV2023 (few-shot object counting).

2022/07 - I joined Sun Yat-sen University as an associate professor.

2022/06 - Paper accepted by ACM MM 2022 (multi-object tracking).

2022/06 - Paper accepted by KBS (generative model for facial expression recognition).

2022/05 - TPAMI paper accepted (face hallucination).

2022/05 - Paper accepted by IJCV (image deblurring survey).

2022/04 - Paper accepted by IJCV (image deraining).

2022/03 - Paper accepted by CVPR2022 (aesthetic text logo synthesis).

2022/01 - TPAMI paper accepted (video deraining).

2021/09 - TIP paper accepted (image deraining).

2021/08 - Invited to serve as a Senior PC member for AAAI 2022.

2021/08 - TIP paper accepted (image desnow).

2021/07 - Paper accepted by ICCV2021 (image SR benchmarking).

Recent Projects

APPTracker: Improving Tracking Multiple Objects in Low-Frame-Rate Videos,
T. Zhou, W. Luo, Z. Shi, J. Chen, Q. Ye,
The 30th ACM International Conference on Multimedia (ACM MM), 2022.

A new perspective to implementing multi-object tracking in edge device.

Fine-grained Image-to-Image Transformation towards Visual Recognition,
W. Xiong, Y. He, Y. Zhang, W. Luo, L. Ma, J. Luo,
Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), USA, 2020.

Image-to-image translation with fine-grained category towards maintaining identity information.

Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis,
W. Liu+, Z. Piao, J. Min, W. Luo, L. Ma, S. Gao,
Proc. of International Conference on Computer Vision (ICCV), Korea, 2019.

A unified framework for human motion transfer, appearance transfer and novel view synthesis using GAN.

End-to-end Active Object Tracking via Reinforcement Learning,
W. Luo*, P. Sun*, F. Zhong, W. Liu, T. Zhang and Y. Wang,
International Conference on Machine Learning (ICML), Sweden, 2018.

The first work proposing the active visual tracking problem and addressing it with reinforcement learning.

Learning to Generate Time-Lapse Videos Using Multi-Stage Dynamic Generative Adversarial Networks,
W. Xiong+, W. Luo, L. Ma, W. Liu and J. Luo,
Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), USA, 2018.

A multi-stage GAN framework to generate time-lapse video given a single image.

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