Staff profile

Conference Paper

• ViTE: Virtual Graph Trajectory Expert Router for Pedestrian Trajectory Prediction
  
  Li, R., Zhu, Z., Qiao, T., & Shum, H. P. H. (in press). ViTE: Virtual Graph Trajectory Expert Router for Pedestrian Trajectory Prediction. Presented at AAAI-26: The 40th Annual AAAI Conference on Artificial Intelligence, Singapore.
• From Category to Scenery: An End-to-End Framework for Multi-Person Human-Object Interaction Recognition in Videos
  
  Qiao, T., Li, R., Li, F. W. B., & Shum, H. P. H. (2025). From Category to Scenery: An End-to-End Framework for Multi-Person Human-Object Interaction Recognition in Videos. In Pattern Recognition 27th International Conference, ICPR 2024, Kolkata, India, December 1–5, 2024, Proceedings, Part XV (pp. 262-277). Springer. https://doi.org/10.1007/978-3-031-78354-8_17
• Multiclass-SGCN: Sparse Graph-based Trajectory Prediction with Agent Class Embedding
  
  Li, R., Katsigiannis, S., & Shum, H. P. (2022). Multiclass-SGCN: Sparse Graph-based Trajectory Prediction with Agent Class Embedding. In 2022 IEEE International Conference on Image Processing (ICIP) Proceedings (pp. 2346-2350). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/icip46576.2022.9897644

Journal Article

• Geometric Visual Fusion Graph Neural Networks for Multi-Person Human-Object Interaction Recognition in Videos
  
  Qiao, T., Li, R., Li, F. W. B., Kubotani, Y., Morishima, S., & Shum, H. P. H. (2025). Geometric Visual Fusion Graph Neural Networks for Multi-Person Human-Object Interaction Recognition in Videos. Expert Systems With Applications, 290, Article 128344. https://doi.org/10.1016/j.eswa.2025.128344
• BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory Prediction
  
  Li, R., Katsigiannis, S., Kim, T., & Shum, H. P. H. (2025). BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory Prediction. IEEE Transactions on Neural Networks and Learning Systems, 36(8), 14566-14580. https://doi.org/10.1109/TNNLS.2025.3545268
• Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction
  
  Li, R., Qiao, T., Katsigiannis, S., Zhu, Z., & Shum, H. P. (2025). Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction. IEEE Transactions on Circuits and Systems for Video Technology, 35(7), 7047-7060. https://doi.org/10.1109/TCSVT.2025.3539522