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Wanli Ouyang, Ph.D.
Professor at Shanghai AI Laboratory
I'm with MMlab and SIGMA lab



Wanli Ouyang obtained Ph.D from the Dept. of Electronic Engineering , the Chinese University of Hong Kong. He is now a professor at Shanghai AI Lab (a new and exciting research lab). He was awarded Australian Research Council Future Fellowship. His research interests include AI4Science, computer vision, and pattern recognition.

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Information for potential Postdoctoral Fellow, Master and Ph.D. students and Final Year Program students

I moved to Shanghai AI Lab . If you are interested in my research topic and this lab, please feel free to contact me after reading the information available here .


Moved to Shanghai AI Lab.

Selected as the rising star by The Australian Newspaper. Detailed information is available here here.

Granted "Vice-Chancellor's Award for Outstanding Research" by The University of Sydney. Detailed information is available here here.

Australian Research Council Future Fellowship (only 9 researchers from The University of Sydney was awarded in 2021)

AI 2000 Most Influential Scholars Honorable Mention in Computer Vision (only 3 recipients in Australia)


My recent talk on 'From Manual Design to Automatic Deep Learning'

My recent tutorial on 'Deep learning in object detection' at PRCV 2019

My recent talk on ‘Structured deep learning for visual localization and recognition’

My talk ‘Modeling deep structures for 3D scene understanding’ at ACCV 2018 workshop

My talk ‘Modeling deep structures for using high performance images’ at ACCV 2018 workshop

Good resources on Paper Writing

How to do good research on computer vision (Chinese)
Making Data meaningful
Slides on "How to get your paper rejected." By Prof. Ming-Hsuan Yang from UC Merced
Chinese blog on how to publish a top journal

Good advices for Research Students

How to write a good review?
How to be good CVPR reviewer? A good Area Chair? A good author?
Three Sins of Authors in Computer Science and Math
How to give research talk (Simon Peyton Jones, co-creator of the C-- programming language)
How to write great research paper(Simon Peyton Jones, co-creator of the C-- programming language)
How to read paper(Harry Shum)
如何写/审AI领域的论文 VALSE Webinar
研究生导师:这种学生,才是我眼中的科研好苗子!(Advice in Chinese)

Chef's Recommendation on Recent Papers

Boyu Chen, Peixia Li, Chuming Li, Baopu Li, Lei Bai, Chen Lin, Ming Sun, Junjie yan, Wanli Ouyang, "GLiT: Neural Architecture Search for Global and Local Image Transformer", Proc. ICCV, 2021. [Full Text] [Video]

Boyu Chen, Peixia Li, Baopu Li, Chen Lin, Chuming Li, Ming Sun, Junjie Yan, Wanli Ouyang, "BN-NAS: Neural Architecture Search with Batch Normalization", Proc. ICCV, 2021. [Full Text]

Yuanzheng Ci, Chen Lin, Ming Sun, Boyu Chen, Hongwen Zhang, Wanli Ouyang, "Evolving Search Space for Neural Architecture Search", Proc. ICCV, 2021. [Full Text]

Yan Lu, Xinzhu Ma, Lei Yang, Tianzhu Zhang, Yating Liu, Qi Chu, Junjie Yan, Wanli Ouyang, "Geometry Uncertainty Projection Network for Monocular 3D Object Detection", Proc. ICCV, 2021. [Full Text] [Source code]

Xinzhu Ma, Yinmin Zhang, Dan Xu, Dongzhan Zhou, Shuai Yi, Haojie Li, Wanli Ouyang, "Delving into Localization Errors for Monocular 3D Object Detection", Proc. CVPR, 2021. [Full Text] [Source code]

Shixiang Tang, Dapeng Chen, Lei Bai, Kaijian Liu, Yixiao Ge, Wanli Ouyang, "Mutual CRF-GNN Network for Few-shot Learning", Proc. CVPR, 2021. [Full Text]

Shixiang Tang, Dapeng Chen, Jinguo Zhu, Shijie Yu, Wanli Ouyang, "Layerwise Optimization by Gradient Decomposition for Continual Learning", Proc. CVPR, 2021. [Full Text]

Dongzhan Zhou, Xinchi Zhou, Hongwen Zhang, Shuai Yi, Wanli Ouyang, "Cheaper Pre-training Lunch: An Efficient Paradigm for Object Detection", Proc. ECCV, 2020. [Full Text]

Dongzhan Zhou, Xinchi Zhou, Wenwei Zhang, Chen Change Loy, Shuai Yi, Xuesen Zhang, Wanli Ouyang, “EcoNAS: Finding Proxies for Economical Neural Architecture Search”, Proc. CVPR, 2020.[Full Text]

Xinzhu Ma, Shinan Liu, Zhiyi Xia, Hongwen Zhang, Xingyu Zeng, Wanli Ouyang, "Rethinking Pseudo-LiDAR Representation", Proc. ECCV, 2020. [Full Text] [Source code]

Our recent survey on object detection:
Liu, Li, Wanli Ouyang, Xiaogang Wang, Paul Fieguth, Jie Chen, Xinwang Liu, and Matti Pietikäinen, "Deep learning for generic object detection: A survey," IJCV, accepted, 2019. [Full Text]

Chef's Recommendation on Previous Papers

Outstanding Young Author Award of the IEEE CAS Society in 2020 for the first work on deep learning winning the ImageNet Large-scale Video Object Detection Challenge (corresponding author).

Kai Kang, Hongsheng Li, Junjie Yan, Xingyu Zeng, Bin Yang, Tong Xiao, Cong Zhang, Zhe Wang, Ruohui Wang, Xiaogang Wang, Wanli Ouyang, "T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos", IEEE Transactions on Circuits and Systems for Video Technology (CSVT) 2017.[Full Text]

Rui Zhao Wanli Ouyang, and Xiaogang Wang, "Unsupervised Salience Learning for Person Re-identification", Proc. IEEE CVPR 2013 (CVPR/ICCV most influential paper)

Wanli Ouyang, Xiaogang Wang, "Joint Deep Learning for Pedestrian Detection", Proc. IEEE ICCV 2013 (CVPR/ICCV most influential paper)

A new back-bone deep model design (performs better than ResNet and DenseNet):
Shuyang Sun, Jiangmiao Pang, Jianping Shi, Shuai Yi, Wanli Ouyang, "FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction," NuerIPS. (Previously called NIPS), 2018. [Full Text] [ Source code ]

Details on our wining entry in ImageNet 2016 challenge on object detection:
Xingyu Zeng (equal contribution), Wanli Ouyang (equal contribution), Junjie Yan, Hongsheng Li, Tong Xiao, Kun Wang, Yu Liu, Yucong Zhou, Bin Yang, Zhe Wang, Hui Zhou, Xiaogang Wang, "Crafting GBD-Net for Object Detection," IEEE Trans. Pattern Anal. Mach. Intell. (PAMI), accepted, 2017. [Full Text] [Project page & code ] [ Source code ]

The first work modeling deformation in deep CNN, used for pedestrian detection:
Wanli Ouyang, Hui Zhou, Hongsheng Li, Quanquan Li, Junjie Yan, Xiaogang Wang, "Jointly learning deep features, deformable parts, occlusion and classification for pedestrian detection," IEEE Trans. Pattern Anal. Mach. Intell. (PAMI), 40(8):1874-1887, 2018. [Full Text] [Source code]

Extend our work on modeling deformation for generic object detection. This new deformation handling layer can be placed anywhere.
Wanli Ouyang, Xingyu Zeng, Xiaogang Wang, et al, "DeepID-Net: Object Detection with Deformable Part Based Convolutional Neural Networks," IEEE Trans. Pattern Anal. Mach. Intell. (PAMI), accepted, 2016. [Full Text] [Project]

A simple and effective multi-scale feature operation. Showing and solving the initialization problem in existing multi-branch networks, e.g. Inception V2-V5, Hourglass, ResNxt, etc.
Wei Yang, Shuang Li, Wanli Ouyang, Hongsheng Li, XiaogangWang. "Learning Feature Pyramids for Human Pose Estimation", Proc. ICCV, 2017. [Full Text] [Source code]

The first work on structured feature learning.
X. Chu, Wanli Ouyang , H. Li, and X. Wang. "Structured feature learning for pose estimation", In Proc. CVPR 2016. [Full Text] [Project and dataset ] [Spotlight talk] [Source code ] [Supplementary ]

The first Fully Convolutional Network for visual tracking.
Lijun Wang, Wanli Ouyang, Xiaogang Wang, and Huchuan Lu, "Visual Tracking with Fully Convolutional Networks", In Proc. ICCV 2015. [Full Text] [Project and source code ]

Videos for Recent Papers

Talk recorded videos for recent papers. [Youtube] [Bilibili]

Journal Papers

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Conference Papers

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