Professional Title / Position：

Professor


Research Direction：

Bioinformatics, Synthetic Biology, Computer Vision, Image Generation, Large Language Models


Team：

Big Data and Visual Computing Team


Email：

cswusi@scut.edu.cn


 Biography：

Si Wu is a Professor and Ph.D. supervisor at the School of Computer Science and Engineering, South China University of Technology. He serves as a member of the Machine Learning Technical Committee of the Chinese Association for Artificial Intelligence (CAAI). He has published 130+ papers in leading international journals and conferences, including over 80 in JCR Q1 venues and CCF-A venues. He has led and participated in more than 10 research projects funded by the National Natural Science Foundation of China and the Guangdong Natural Science Foundation, among others. His honors include the Second Prize of the Guangdong Provincial Science and Technology Progress Award (2019). He also supervised graduate students who won the Gold Award (Industrial Track) at the 7th China International “Internet+” College Students Innovation and Entrepreneurship Competition.


 Education：

 2009–2012 City University of Hong Kong, Ph.D.

 2006–2008 Huazhong University of Science and Technology, M.Sc.

 2002–2006 Huazhong University of Science and Technology, B.Sc.


 Work Experience：

2014–Present School of Computer Science and Engineering, South China University of Technology — Associate Professor, Professor

2013–2014 School of Electrical Engineering and Computer Science, University of Ottawa, Canada — Postdoctoral Research Fellow


 Course：

Pattern Recognition (taught in English)


Algorithm Design and Analysis


 Publications：

  1.  Y. Zhang, J. Wang, Y. Huang, T. Chen, H. Wong, and S. Wu, “ClassBooth: boost class semantics with bidirectional feature fusion in text-to-image diffusion models,” IEEE Transactions on Multimedia, 2025.  

  2. T. Chen, H. Fu, H. Wong, Y. Huang, S. Wu, Y. Xu, D. Wu, “3DMM-GAN: multi-modal alignment with adversarial learning for compositional 3D human image synthesis,” IEEE Transactions on Emerging Topics in Computational Intelligence, 2025.

  3. J. Zhang, X. Li, S. Wu, Y. Xu, and Y. Wang, “Prior-free augmentation for cloth-changing person re-identification,” ACM International Conference on Multimedia (MM), 2025.

  4. W. Chen, Z. Xu, R. Xu, S. Wu, and H. Wong, “Task-aware cross-model feature refinement transformer with large language models for visual grounding,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025.  

  5. L. Xie, B. Zheng, S. Wu, and H. Wong, “Dynamic content prediction with motion-aware priors for blind face video restoration,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025.

  6. T. Chen, Y. Zhang, L. Xie, W. Shen, S. Wu, and H. Wong, “SpotDiff: spatial gene expression imputation diffusion with single-cell RNA sequencing data integration,” AAAI Conference on Artificial Intelligence (AAAI), 2025.

  7. F. Xu, T. Chen, F. Yang, Y. Zhang, and S. Wu, “3DHumanEdit: multi-modal body part-aware conditioning information integration for 3D human manipulation,” AAAI Conference on Artificial Intelligence (AAAI), 2025.

  8. W. Xue, C. Ding, R. Xu, S. Wu, Y. Xu, and H. Wong, “RetouchGPT: LLM-based interactive high-fidelity face retouching via imperfection prompting,” AAAI Conference on Artificial Intelligence (AAAI), 2025.

  9. L. Xie, B. Zheng, W. Xue, Y. Zhang, L. Jiang, R. Xu, S. Wu, and H. Wong, “Discrete prior-based temporal-coherent content prediction for blind face video restoration,” AAAI Conference on Artificial Intelligence (AAAI), 2025.

  10. S. Pan, Y. Xu, R. Xu, Z. Zhou, S. Wu, and Z. Yu, “Self-correcting robot manipulation via Gaussian-splatted foresight,” AAAI Conference on Artificial Intelligence (AAAI), 2025.

  11. C. Liu, R. Li, S. Wu, H. Che, M. Leung, Z. Yu, and H. Wong, “Beyond Euclidean structures: collaborative topological graph learning for multi-view clustering,” IEEE Transactions on Neural Network and Learning Systems, 2024.

  12.  L. Jiang, Y. Huang, L. Xie, W. Xue, C. Liu, S. Wu, and H. Wong, “Hunting blemishes: language-guided high-fidelity face retouching transformer with limited paired data,” ACM International Conference on Multimedia (MM), 2024.

  13. X. Wang, H. Gao, X. Wei, L. Peng, R. Li, C. Liu, S. Wu, and H. Wong, “Contrastive graph distribution alignment for partially view-aligned clustering,” ACM International Conference on Multimedia (MM), 2024.

  14. C. Liu, R. Li, H. Che, M. Leung, S. Wu, Z. Yu, and H. Wong, “Latent structure-aware view recovery for incomplete multi-view clustering,” IEEE Transactions on Knowledge and Data Engineering, 2024.

  15.  L. Lin, W. Xue, X. Wei, W. Shen, C. Liu, S. Wu, and H. Wong, “SCTrans: Multi-scale scRNA-seq sub-vector completion transformer for gene-selective cell type annotation,” International Joint Conference on Artificial Intelligence (IJCAI), 2024.

  16. H. Gao, W. Shen, R. Li, C. Liu, and S. Wu, “Collaborative structure-preserved missing data imputation for single-cell RNA-Seq clustering,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2024.

  17.  A. GNANHA, W. Cao, X. Mao, S. Wu, H. Wong, and Q. Li, “EviD-GAN: Improving GAN with an infinite set of discriminators at negligible cost,” IEEE Transactions on Neural Networks and Learning Systems, 2024.

  18. W. Wu, H. Wong, and S. Wu, “Pseudo-Siamese teacher for semi-supervised oriented object detection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-14, 2024.

  19.  L. Xie, B. Zheng, W. Xue, L. Jiang, S. Wu, C. Liu, and H. Wong, “Learning degradation-unaware representation with prior-based latent transformations for blind face restoration,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024.

  20.  W. Xue, L. Jiang, L. Xie, S. Wu, Y. Xu, and H. Wong, “VRetouchEr: learning cross-frame feature interdependence with imperfection flow for face retouching in videos,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024.

  21. W. Wu, H. Wong, S. Wu, and T. Zhang, “Relational matching for weakly semi-supervised oriented object detection,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024.

  22. F. Xu, R. Li, Si Wu, Y. Xu, and H. Wong, “Text-conditional attribute alignment across latent spaces for 3D controllable face image synthesis,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024.

  23.  F. Yang, T. Chen, X. He, Z. Cai, L. Yang, S. Wu, and G. Lin, “AttriHuman-3D: editable 3D human avatar generation with attribute decomposition and indexing,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024.

  24. W. Xue, L. Xie, L. Jiang, T. Chen, S. Wu, C. Liu, and H. Wong, “RetouchFormer: semi-supervised high-quality face retouching transformer with prior-based selective self-attention,” AAAI Conference on Artificial Intelligence (AAAI), 2024.

  25.  Q. Song, J. Li, S. Wu, and H. Wong, “A graph-based discriminator architecture for multi-attribute facial image editing,” IEEE Transactions on Multimedia, vol. 26, pp. 436-446, 2023.

  26.  Y. Zhang, X. Huo, T. Chen, S. Wu, and H. Wong, “Exploring intra-class variation factors with learnable prompts for semi-supervised image synthesis,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

  27. L, Xie, W. Xue, Z. Xu, S. Wu, Z. Yu, and H. Wong, “Blemish-aware and progressive face retouching with limited paired data,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

  28. X, Wei, Z. Xu, C. Liu, S. Wu, Z. Yu, and H. Wong, “Text-guided unsupervised latent transformations for multi-attribute image manipulation,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

  29. W. Wu, S. Wu, and H. Wong, “Semi-supervised stereo-based 3D object detection via cross-view consensus,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

  30. C. Liu, R. Li, H. Che, S. Wu, D. Jiang, Z. Yu, and H. Wong, “Self-guided partial graph propagation for incomplete multi-view clustering,” IEEE Transactions on Neural Networks and Learning Systems, 2022

  31. C. Liu, S. Wu, R. Li, D. Jiang, and H. Wong, “Self-supervised graph completion for incomplete multi-view clustering,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 9, pp. 9394-9406, 2023.

  32.  T. Chen, Y. Zhang, X. Huo, S. Wu, Y. Xu, and H. Wong, “SphericGAN: Semi-supervised hyper-spherical generative adversarial networks for fine-grained image synthesis,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.

  33. C. Liu, S. Wu, D. Jiang, Z. Yu, and H. Wong, “View-aware collaborative learning for survival prediction and subgroup identification,” IEEE Transactions on Biomedical Engineering, vol. 70, no. 1, pp. 307-317, 2022.

  34. J. Zhong, X. Zeng, W. Cao, S. Wu, Z. Yu, and H. Wong, “Semi-supervised multiple choice learning for ensemble classification,” IEEE Transactions on Cybernetics, vol. 52, no. 5, pp. 3658-3668, 2022.

  35. C. Liu, W. Cao, S. Wu, W. Shen, D. Jiang, Z. Yu, and H. Wong, “Supervised graph clustering for cancer subtyping based on survival analysis and integration of multi-omic tumor data,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 19, no. 2, pp. 1193-1202, 2022.

  36. S. Lin, W. Wu, S. Wu, Y. Xu, and H. Wong, “Unreliable-to-reliable instance translation for semi-supervised pedestrian detection,” IEEE Transactions on Multimedia, vol. 24, pp. 728-738, 2022.

  37. C. Liu, W. Cao, S. Wu, W. Shen, D. Jian, Z. Yu, and H. Wong, “Asymmetric graph-guided multitask survival analysis with self-paced learning,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 2, pp. 654-666, 2022.

  38. T. Chen, Y. Liu, Y. Zhang, S. Wu, Y. Xu, L. Feng, and H. Wong, “Semi-supervised single-stage controllable GANs for conditional fine-grained image generation,” IEEE/CVF International Conference on Computer Vision (ICCV), 2021.

  39. G. Li, Y. Liu, X. Wei, Y. Zhang, S. Wu, Y. Xu, and H. Wong, “Discovering density-preserving latent space walks in GANs for semantic image transformations,” ACM International Conference on Multimedia (MM), 2021.

  40. T. Chen, S. Wu, X. Yang, Y. Xu, and H. Wong, “Semantic regularized class-conditional GANs for semi-supervised fine-grained image synthesis,” IEEE Transactions on Multimedia, vol. 24, pp. 2975-2985, 2022.

  41. H. Zhou, M. Azzam, J. Zhong, C. Liu, S. Wu, and H. Wong, “Knowledge exchange between domain-adversarial and private networks improves open set image classification,” IEEE Transactions on Image Processing, vol. 30, pp. 5807-5818, 2021.

  42. Y. Liu, X. Huo, T. Chen, X. Zeng, S. Wu, Z. Yu, and H. Wong, “Mask-embedded discriminator with region-based semantic regularization for semi-supervised class-conditional image synthesis,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.

  43. G. Li, Q. Jiao, S. Qian, S. Wu, and H. Wong, “High fidelity GAN inversion via prior multi-subspace feature composition,” in AAAI Conference on Artificial Intelligence (AAAI), 2021.

  44. M. Azzam, W. Wu, W. Cao, S. Wu, and H. Wong, “KTransGAN: variational inference-based knowledge transfer for unsupervised conditional generative learning,” IEEE Transactions on Multimedia, vol. 23, pp. 3318-3331, 2020.

  45. R. Li, W. Cao, H. Wong, and S. Wu, “Generating target image-label pairs for unsupervised domain adaptation,” IEEE Transactions on Image Processing, vol. 29, pp. 7997-8011, 2020.

  46. Y. Liu, G. Deng, X. Zeng, S. Wu, Z. Yu, and H. Wong, “Regularizing discriminative capability of CGANs for semi-supervised generative learning,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.

  47. R. Li, Q. Jiao, W. Cao, H. Wong and S. Wu, “Model adaptation: unsupervised domain adaptation without source data,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.

  48. S. Wu, W. Wu, S. Lei, S. Lin, R. Li, Z. Yu and H. Wong, “Semi-supervised human detection via region proposal networks aided by verification,” IEEE Transactions on Image Processing, vol. 29, pp. 1562-1574, 2020.

  49. J. Li, S. Wu, C. Liu, Z. Yu and H. Wong, “Semi-supervised deep coupled ensemble learning with classification landmark exploration,” IEEE Transactions on Image Processing, vol. 29, pp. 538-550, 2020.

  50. C. Liu, C. Zheng, S. Wu, Z. Yu, and H. Wong, “Multitask feature selection by graph-clustered feature sharing,” IEEE Transactions on Cybernetics, vol. 50, no. 1, pp. 74-86, 2020.