董伟生，男，1981年4月生于浙江省兰溪市，人工智能学院，二级教授，博导，副院长，国家级人才。主要从事图像视频表征与处理、多模态大模型、计算机视觉方面的研究工作。在权威国际期刊和会议上发表论文180余篇，其中在TPAMI、IJCV、IEEE-TIP、CVPR、NeurIPS等顶级期刊和会议上发表论文80多篇。论文被引用12000余次，2篇论文单篇引用超过1500余次。曾任/现任包括国际顶级期刊IEEE Trans. on Image Processing、SIAM Journal on Imaging Sciences在内的3个期刊的编委(Associate Editor)、CVPR 2022领域主席。主持包括国家部委重大项目、国家优青、国家自然科学基金重大项目课题等项目。曾多次获得国家级青年人才称号。以第二完成人身份获2017年国家自然科学二等奖、2013年陕西省科学技术一等奖；曾获2017年陕西省自然科学论文一等奖，VCIP 2010最佳论文奖。近期研究成果详见个人主页：https://see.xidian.edu.cn/faculty/wsdong

招生信息：2025年杭研院还有2个统考指标，欢迎预录取同学联系！wsdong@mail.xidian.edu.cn

教育经历：

• 2000.9~2004.6            华中科技大学电子信息工程系                      工学学士

• 2004.9~2010.8            西安电子科技大学电子工程学院                  工学博士

工作经历

• 2018.1~至今                 西安电子科技大学人工智能学院           教授

• 2016.7~2017.12            西安电子科技大学电子工程学院           教授

• 2012.6~2016.6              西安电子科技大学电子工程学院           副教授

• 2012.8~2013.2              微软亚洲研究院视觉计算组                   客座研究员

• 2010.9~2012.6              西安电子科技大学电子工程学院           讲师

• 2009.1~2010.6              香港理工大学计算学系                           Research Assistant

学术服务：

• IEEE Transactions on Image Processing ,  编委(Associate Editor)，07/2015~2019.7

• SIAM Journal on Imaging Science，编委(Associate Editor)，01/2017~至今

• Circuits, System and Signal Processing,  编委(Associate Editor)，2014~至今

近期录用和发表的代表性论文：

[1]    Zhenxuan Fang, Fangfang Wu, Tao Huang, Le Dong, Weisheng Dong, Xin Li, Guangming Shi, “Parameterized Blur Kernel Prior Learning for Local Motion Deblurring”, CVPR 2025.

[2]    Zhou Yang, Mingtao Feng, Tao Huang, Fangfang Wu, Weisheng Dong, Xin Li, Guangming Shi, “Gain from Neighbors: Boosting Model Robustness in the Wild via Adversarial Perturbations Toward Neighboring Classes”, CVPR 2025.

[3]    Jinghao Bian, Mingtao Feng, Weisheng Dong, Fangfang Wu, Jianqiao Luo, Yaonan Wang, Guangming Shi, “Feature Information Driven Position Gaussian Distribution Estimation for Tiny Object Detection”, CVPR 2025.

[4]    Qitong Yang, Mingtao Feng, Zijie Wu, Weisheng Dong, Fangfang Wu, Yaonan Wang, Ajmal Saeed Mian, “Hierarchical Gaussian Mixture Model Splatting for Efficient and Part Controllable 3D Generation”, CVPR 2025.

[5]    Fangfang Wu, Tao Huang, Junwei Xu, Xun Cao, Weisheng Dong, Le Dong and Guangming Shi, “Joint spatial and frequency domain learning for lightweight spectral image demosaicing,” IEEE Trans. on Image Processing, 2025.

[6]    Y. Sun, G. Shi, W. Dong, X. Li, L. Dong, and X. Xie, “local uncertainty energy transfer for active domain adaptation,” IEEE Trans. on Image Processing,  2025. (Paper, project & code)

[7]    Q. Tan, A. Li, L. Dong, W. Dong, X. Li and G. Shi, "CDS-Net: Contextual Difference Sensitivity Network for Pixel-Wise Road Crack Detection," IEEE Transactions on Circuits and Systems for Video Technology, 2024. (Paper, project & code)

[8]    Mingtao Feng, Fenghao Tian, Jianqiao Luo, Zijie Wu, Weisheng Dong, Yaonan Wang, Ajmal Mian, “Semantic Ambiguity Modeling and Propagation for Fine-Grained Visual Cross View Geo-Localization”, AAAI 2025. (Paper, project & code)

[9]    Junwei Xu, Tao Huang, Zhenyu Wang, Weisheng Dong, and Xin Li, “Bridging Task Boundaries: Remote Sensing ImageText Retrieval via Dictionary-Driven Adaptation,”  in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2025. (Paper, project & code)

2024:

[10] L. Dong, M. Liu, T. Tang, T. Huang, J. Lin, W. Dong, and G. Shi, “Spatial-Spectral Mixing Transformer With Hybrid Image Prior for Multispectral Image Demosaicing”, IEEE Journal of Selected Topics in Signal Processing, 2024. (Paper, project & code)

[11] Chenbo Yan, Mingtao Feng, Zijie Wu, Yulan Guo, Weisheng Dong, Yaonan Wang and Ajmal Mian, “Discriminative Correspondence Estimation for Unsupervised RGB-D Point Cloud Registration,” IEEE Trans. on Circuits and Systems for Video Technology, 2024. (Paper, project & code)

[12] L. Sun, J. Lin, W. Dong, X. Li, J. Wu, and G. Shi, “Learning real-world heterogeneous noise models with a benchmark dataset,” Pattern Recognition, 2024. (Paper, Project & code)

[13] Lu Sun, Fangfang Wu, Wei Ding, Xin Li, Weisheng Dong, and Guangming Shi, “Multi-scale spatio-temporal memory network for lightweight video denoising,” IEEE Trans. on Image Processing, 2024. (Paper, project & code)

[14] Y. Zhu, R. Ren, W. Dong, X. Li and G. Shi, “TSUDepth: exploring temporal symmetry-based uncertainty for unsupervised monocular depth estimation,” Neurocomputing, 2024. (Paper, project & code)

[15] Zijie Wu, Mingtao Feng, Yaonan Wang, He Xie, Weisheng Dong, Bo Miao, and Ajmal Mian, External Knowledge Enhanced 3D Scene Generation from Sketch, ECCV 2024. (Paper)

[16] Bokang Wang, Qian Ning, Fangfang Wu, Xin Li, Weisheng Dong and Guangming Shi, “Uncertainty modeling of the transmission map for single image dehazing”, IEEE Trans. on Circuits and Systems for Video Technology, 2024 (Paper, project & code)

[17] Wenqi Dang, Zhou Yang, Weisheng Dong, Xin Li, and Guangming Shi, “Inverse weight-balancing for deep long-tailed learning”, AAAI 2024. (Paper, project & code)

[18] Yulin Sun, Weisheng Dong, Xin Li, Le Dong, Guangming Shi, and Xuemei Xie, “TransVQA: transferable vector quantization alignment for unsupervised domain adaption”, IEEE Trans. on Image Processing, vol. 33, pp. 856-866, 2024. (Paper, project & code)

2023:

[19] Q. Ning, F. Wu, W. Dong, X. Li, and G. Shi, “Exploring Correlations in Degraded Spatial Identity Features for Blind Face Restoration,” ACM Multimedia, 2023. (Paper, project & code)

[20] Y. Liu, T. Huang, W. Dong*, X. Li, and G. Shi, “Low-Light image enhancement with multi-stage residue quantization and brightness-aware attention,” IEEE ICCV, 2023. (Paper, project & code)

[21] J. Xu, F. Wu, X. Li, W. Dong, T. Huang, and G. Shi, “Spatially varying prior learning for blind hyperspectral image fusion,” IEEE Trans. on Image Processing, vol. 32, pp. 4416-4431, 2023. (Paper, project & code)

[22] T. Huang, W. Dong*, F. Wu, X. Li, and G. Shi, “Uncertainty-driven knowledge distillation for language model compression,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 31, pp. 2850-2858, 2023. (Paper, project & code)

[23] C. Wang, W. Dong*, X. Li, F. Wu, J. Wu, and G. Shi, “Memory based temporal fusion network for video deblurring,” International Journal of Computer Vision, vol. 131, pp. 1840-1856, 2023. (Paper, project & code)

[24] T. Huang, X. Yuan, W. Dong*, J. Wu, and G. Shi, “Deep Gaussian Scale Mixture Prior for Image Reconstruction,” IEEE Trans. on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 45, no. 9, pp. 10778-10794, Sep. 2023. (Paper, project & code)

[25] Z. Yang, W. Dong*, X. Li, Y. Sun, M. Huang and G. Shi, “Vector Quantization with Self-attention for Quality-independent Representation Learning”, IEEE CVPR 2023. (Paper, project & code)

[26] Z. Fang, F. Wu, W. Dong, X. Li, J. Wu and G. Shi, “Self-supervised non-uniform kernel estimation with flow-based motion prior for blind image deblurring,” IEEE CVPR 2023. (Paper, project & code)

[27] Chengxing Xie, Qian Ning, Weisheng Dong, Guangming Shi, “TFRGAN: Leveraging Text Information for Blind Face Restoration with Extreme Degradation”, CVPR Workshops, pp. 2535-2545, 2023.

[28] X. Lu, W. Dong*, X. Li, J. Wu, L. Li, and G. Shi, “Adaptive search-and-training for robust and efficient network pruning,” IEEE Trans. on Pattern Analysis and Machine Intelligence (T-PAMI), 2023. (Paper, project & code)

[29] Xin Li, Weisheng Dong, Jinjian Wu, Leida Li, Guangming Shi, “Super-resolution Image Reconstruction: Selective milestones and open problems”, IEEE Signal Process. Mag., vol. 40, no. 5, pp. 54-66, 2023. (Paper)

[30] L. Sun, Y. Wang, F. Wu, X. Li, W. Dong, and G. Shi, “Deep unfolding network for efficient mixed video noise removal,” IEEE Trans. on Circuits and System for Video Technology (T-CSVT), vol. 33, no. 9, pp. 4715-4727, 2023. (Paper, project & code)

[31] Q. Ning, W. Dong*, X. Li and J. Wu, “Searching efficient model-guided deep network for image denoising,” IEEE Trans. on Image Processing, vol. 23, pp. 668-681, 2023. (Paper, project & code).

[32] Han Huang, Li Shen, Chaoyang He, Weisheng Dong, Wei Liu, “Differentiable Neural Architecture Search for Extremely Lightweight Image Super-Resolution”, IEEE Trans. Circuits Syst. Video Technol., vol. 33, no. 6, pp. 2672-2682, 2023.

[33] Mengluan Huang, Le Dong, Weisheng Dong, Guangming Shi, “Supervised Contrastive Learning Based on Fusion of Global and Local Features for Remote Sensing Image Retrieval”, IEEE Trans. Geosci. Remote. Sens., vol. 61, pp. 1-13, 2023.

[34] W. Dong, J. Wu, L. Li, G. Shi, and X. Li, “Bayesian deep learning for image reconstruction: from structured sparsity to uncertainty estimation,” IEEE Signal Processing Magazine, vol. 40, no. 1, pp. 73-84, 2023. (Paper)

2022:

[35] Z. Fang, W. Dong*, X. Li, J. Wu, L. Li, and G. Shi, “Uncertainty learning in kernel estimation for multi-stage blind image super-resolution,” ECCV 2022. (Paper, project & code) (A novel kernel estimation method was proposed with uncertainty learning, achieving SOTA blind image SR results.)

[36] Z. Yang, W. Dong*, X. Li, J. Wu, L. Li, and G. Shi, “Self-feature distillation with uncertainty modeling for degraded image recognition,” ECCV 2022. (Paper, project & code) (A weighted feature distillation loss with uncertainty learning was proposed for degraded image recognition.)

[37] X. Lu, T. Xi, B. Li, G. Zhang, and W. Dong, “Bayesian based re-parameterization for DNN model pruning,” ACM Multimedia, 2022. (Paper)

[38] Q. Ning, J. Tang, F. Wu, W. Dong*, et al., “Learning degradation Uncertainty for unsupervised real-world image super-resolution,” IJCAI 2022. (Paper, project & code) (Uncertainty-based loss for simulating real LR images for unsupervised real image SR.)

[39] T. Huang, W. Dong*, J. Wu, L. Li, X. Li, and Guangming Shi, “Deep hyperspectral image fusion network with iterative spatio-spectral regularization,” IEEE Trans. on Computational Imaging, in press, 2022. (Paper, project & code)

[40] Y. Zhu, W. Dong*, X Li, J. Wu, L. Li, and G. Shi, “Robust depth completion with uncertainty-driven loss functions,” AAAI 2022. (Paper, project & code)

2021:

[41] Q. Ning, W. Dong*, X. Li, J. Wu, and G. Shi, “Uncertainty-driven loss for single image super-resolution,” NeurIPS 2021. (Paper, project & code)

[42] Y. Cao, G. Shi, T. Zhang, W. Dong*, J. Wu, X. Xie, and X. Li, “Bayesian correlation filter learning with Gaussian scale mixture model for visual tracking”, IEEE Trans. on Circuit and Systems for Video Technology (T-CSVT), vol. 32, no. 5, pp. 3085-3098, 2021. (Paper, Project & Code)

[43] L. Sun, W. Dong*, X. Li, J. Wu, L. Li, and G. Shi, “Deep maximum a posterior estimator for video denoising”, International Journal of Computer Vision (IJCV), vol. 129, pp. 2827–2845, 2021. (Paper, Project & Code) (MAP-based video denoising algorithm was unfolded into a deep network, leading to principle and state-of-the-art video denoising performance!)

[44] W. Dong, C. Zhou, F. Wu, J. Wu, G. Shi, and X. Li, “Model-guided deep hyperspectral image super-resolution,” IEEE Trans. on Image Processing (T-IP), vol. 30, pp. 5754-5768, 2021. (Paper, Project & Code) (A model-guided DCNN was proposed for hyperspectral image super-resolution, obtaining state-of-the-art performance!)

[45] T. Huang, W. Dong*, X. Yuan*, J. Wu, and G. Shi, “Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging,” IEEE CVPR 2021. (Paper, Project & Code) (Deep Gaussian Scale Mixture network was proposed to learn the parametric image distributions, leading to state-of-the-art Spectral image reconstruction performance!)

[46] Q. Ning, W. Dong*, G. Shi, L. Li and X. Li, “Accurate and lightweight image super-resolution with model-guided deep unfolding network,” IEEE Journal of Selected Topics on Signal Processing (J-STSP), vol. 15, no. 2, pp. 240-252, Feb. 2021. (Paper, Code, Github) (A deep nonlocal auto-regressive model is imbedded into the network obtaining state-of-the-art image SR performance!)