Staff profile

Chris (Shuang) Chen is a Postdoctoral Researcher in Durham University’s Department of Computer Science. Their research spans computer vision, visual signal processing, generative models, and large language models, with a focus on building learning systems that understand and synthesise visual information robustly and efficiently. Prior to joining Durham, Chris earned an MSc in Computer Vision, Robotics and Machine Learning from the University of Surrey, and a BEng in Automation from Shandong University of Technology. They are particularly interested in bridging low-level visual signals with high-level reasoning, and in developing multimodal methods that connect images, video, and language. Beyond research, Chris enjoys collaborating across disciplines and mentoring students on projects at the intersection of AI and visual computing.

• Computer Vision, Image Processing, Image Restoration, Image Inpainting.

Conference Paper

• Deep Learning-Enhanced Visual Monitoring in Hazardous Underwater Environments with a Swarm of Micro-Robots
  
  Chen, S., He, Y., Lennox, B., Arvin, F., & Atapour-Abarghouei, A. (in press). Deep Learning-Enhanced Visual Monitoring in Hazardous Underwater Environments with a Swarm of Micro-Robots. Presented at IEEE International Conference on Robotics & Automation, Atlanta, USA.
• Depth-Aware Endoscopic Video Inpainting
  
  Xiatian Zhang, F., Chen, S., Xie, X., & Shum, H. P. (in press). Depth-Aware Endoscopic Video Inpainting. Presented at 27th International Conference on Medical Image Computing and Computer Assisted Intervention, Marrakesh, Morocco.
• SEM-Net: Efficient Pixel Modelling for Image Inpainting with Spatially Enhanced SSM
  
  Chen, S., Zhang, H., Atapour-Abarghouei, A., & Shum, H. P. H. (2025). SEM-Net: Efficient Pixel Modelling for Image Inpainting with Spatially Enhanced SSM. In Proceedings of the 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 461-471). IEEE. https://doi.org/10.1109/WACV61041.2025.00055
• MxT: Mamba x Transformer for Image Inpainting
  
  Chen, S., Atapour-Abarghouei, A., Zhang, H., & Shum, H. P. H. (2024). MxT: Mamba x Transformer for Image Inpainting. In Proceedings of the 2024 British Machine Vision Conference. British Machine Vision Association.
• A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip
  
  Chen, S., Atapour-Abarghouei, A., Kerby, J., Ho, E. S., Sainsbury, D. C., Butterworth, S., & Shum, H. P. (2022). A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip. Presented at 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Ioannina, Greece. https://doi.org/10.1109/bhi56158.2022.9926917

Journal Article

• BOOST: Out-of-distribution-informed adaptive sampling for bias mitigation in stylistic convolutional neural networks
  
  Vijendran, M., Chen, S., Deng, J., & Shum, H. P. H. (2026). BOOST: Out-of-distribution-informed adaptive sampling for bias mitigation in stylistic convolutional neural networks. Expert Systems With Applications, 296(Part A), Article 128905. https://doi.org/10.1016/j.eswa.2025.128905
• Artificial intelligence for geometry-based feature extraction, analysis and synthesis in artistic images: a survey
  
  Vijendran, M., Deng, J., Chen, S., Ho, E. S. L., & Shum, H. P. H. (2025). Artificial intelligence for geometry-based feature extraction, analysis and synthesis in artistic images: a survey. Artificial Intelligence Review, 58(2), Article 64. https://doi.org/10.1007/s10462-024-11051-3
• One-Index Vector Quantization Based Adversarial Attack on Image Classification
  
  Fan, H., Qin, X., Chen, S., Shum, H. P. H., & Li, M. (2024). One-Index Vector Quantization Based Adversarial Attack on Image Classification. Pattern Recognition Letters, 186, 47-56. https://doi.org/10.1016/j.patrec.2024.09.001
• HINT: High-quality INpainting Transformer with Mask-Aware Encoding and Enhanced Attention
  
  Chen, S., Atapour-Abarghouei, A., & Shum, H. P. H. (2024). HINT: High-quality INpainting Transformer with Mask-Aware Encoding and Enhanced Attention. IEEE Transactions on Multimedia, 26, 7649-7660. https://doi.org/10.1109/TMM.2024.3369897
• INCLG: Inpainting for Non-Cleft Lip Generation with a Multi-Task Image Processing Network
  
  Chen, S., Atapour-Abarghouei, A., Ho, E. S., & Shum, H. P. (2023). INCLG: Inpainting for Non-Cleft Lip Generation with a Multi-Task Image Processing Network. Software Impacts, 17, Article 100517. https://doi.org/10.1016/j.simpa.2023.100517