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Dr. Wei Ke

Associate Professor

My research lies in computer vision and machine learning, especially object/pedestrian detection, low-level image processing, and action recognition. 

Contact: wei.ke[at]xjtu.edu.cn

I'm sorry that there is no position for international students right now in my team.

“Young Talent Support Plan” of Xi’an Jiaotong University

“Siyuan Scholar” of Xi’an Jiaotong University

2011.09-2018.06  Ph.D. of Signal and Information Processing, Unversity of Chinese Academy of Sciences. Advised by Prof. Qixiang Ye

2015.10-2016.09  Joint Ph.D. Student, University of Oulu, Finland. Advised by Prof. Guoying Zhao and Prof. Jie Chen

2007.09-2011.06  Bachelor of Electrical Engineering and Automation，Beihang University (BUAA). Advised by Prof. Jing Wu

2020.04-                 Assoicate Professor, Xi'an Jiaotong University

2018.03-2020.03    Postdoc Fellow, Carnegie Mellon University (CMU), advised by Dr. Dong Huang

2017.11-2018.01    Intern, Microsoft Research Asia (MSRA), advised by Dr. Xun Guo

Google Scholar 

2. Y. Wu, T. Zhang, W. Ke*,S. Süsstrunk, M. Salzmann. Spatiotemporal Self-supervised Learning for Point Clouds in the Wild. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

3. Q. Liu#, H. Liu#, W. Ke*, Y. Liang*. Automated Lesion Segmentation in Fundus Images with Many-to-Many Reassembly of Features. Pattern Recognition, 2022.  

4. Z. Zhang#, Y. Zhu#, J. Liu, X. Liang,  W. Ke*. CoupAlign: Coupling Word-Pixel with Sentence-Mask Alignments for Referring Image Segmentation. NeurIPS, 2022.  

5. T. Zhang, C. Qiu, W. Ke*, S. Süsstrunk, M. Salzmann. Leverage Your Local and Global Representations: A New Self-Supervised Learning Strategy. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.  

6. Y. He, Z. Ma, X. Wei, X. Hong, W. Ke, Y. Gong. Error-aware density isomorphism reconstruction for unsupervised cross-domain crowd counting. The AAAI Conference on Artificial Intelligence (AAAI), 2021. 

7. X. Wei, D. Li, X. Hong, W. Ke, Y. Gong. Co-attentive lifting for infrared-visible person re-identification. ACM International Conference on Multimedia, 2020.

8. Z. Luo, D. Guillory, B Shi, W. Ke, F. Wan, T. Darrell, H. Xu. Weakly-supervised action localization with expectation-maximization multi-instance learning. European Conference on Computer Vision (ECCV), 2020

9. W. Ke^#, T. Zhang^#, Z, Huang, Q. Ye*, J. Liu, and D. Huang. Multiple Anchor Learning for Visual Object Detection.  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.  [arXiv] [code]
10. W. Ke, J, Chen, J. Jiao, G. Zhao, and Q. Ye*. SRN: Side-output Residual Network for Object Reflectional Symmetry Detection and Beyond. IEEE Transactions on Neural Networks and Learning Systems (T-NNLS), 2020. [EarlyAccess pdf]
11. Q. Ye, T. Zhang, and W. Ke*. Progressive Latent Models for Self-learning Scene-specific Pedestrian Detectors. IEEE Transactions on Intelligent Transportation Systems (T-ITS), vol. 21, no. 4, pp. 1415-1426, 2020. [pdf]
12. Z. Huang, W. Ke, and D. Huang. Improving Object Detection with Inverted Attention. IEEE Winter Conference on Applications of Computer Vision (WACV), 1294-1302, 2020. [arXiv] [pdf]
13. W. Ke, J, Chen, and Q. Ye. Deep Contour and Symmetry Scored Object Proposal. Pattern Recognition Letters vol. 119, pp. 172-179, 2019. [pdf]
14. F. Wan, F. Liu, W. Ke, et al. C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR oral), 2199-2208, 2019. [pdf]
15. C. Liu, F. Wan, W. Ke, et al. Orthogonal Decomposition Network for Pixel-Wise Binary Classification. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6064-6073, 2019. [pdf]
16. C. Liu^#, W. Ke^#, F. Qin, and Q. Ye. Linear Span Network for Object Skeleton Detection. European Conference on Computer Vision (ECCV), 133-148, 2018. [pdf]
17. W. Ke, J, Chen, J. Jiao, G. Zhao, and Q. Ye. SRN: Side-output Residual Network for Object Symmetry Detection in the Wild. IEEE Conference on Computer Vision and Pattern Recognition (CVPR oral), 1068-1076, 2017. [pdf] [code and dataset] [video]
18. Q. Ye, T. Zhang, W. Ke*, et al. Self-learning Scene-specific Pedestrian Detectors Using a Progressive Latent Model. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 509-518, 2017. [pdf] [poster]
19. W. Ke, Y. Zhang, P. Wei, Q. Ye and J. Jiao. Pedestrian Detection via PCA Filters Based Convolutional Channel Features. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1394-1398, 2015. [pdf]