Published in Neurocomputing, 2025
A new network architecture for EEG emotion recognition utilizing spatial-frequency shifted windows.
Recommended citation: W Ma, Y Li, T Li, H Yang, Z Li, L Wang, J Xuan. (2025). "SFSWTS: A spatial-frequency shifted windows and time self-attention network for EEG emotion recognition." Neurocomputing, 130309.
Published in Pattern Recognition Letters, 2023
An adversarial autoencoder model designed for novelty detection with calibrated reconstruction.
Recommended citation: Y Huang, Y Li, G Jourjon, S Seneviratne, K Thilakarathna, A Cheng, et al. (2023). "Calibrated reconstruction based adversarial autoencoder model for novelty detection." Pattern Recognition Letters, 169, 50-57.
Published in Computer Networks, 2022
A hierarchical approach for classifying encrypted traffic from classes to specific content.
Recommended citation: Y Li, Y Huang, S Seneviratne, K Thilakarathna, A Cheng, G Jourjon, et al. (2022). "From traffic classes to content: A hierarchical approach for encrypted traffic classification." Computer Networks, 212, 109017.
Published in Pattern Recognition Letters, 2022
Using task adaptive siamese neural networks for identifying open-set encrypted network traffic.
Recommended citation: Y Huang, Y Li, T Heyes, G Jourjon, A Cheng, S Seneviratne, et al. (2022). "Task adaptive siamese neural networks for open-set recognition of encrypted network traffic with bidirectional dropout." Pattern Recognition Letters, 159, 132-139.
Published in Proceedings of the 2025 International Conference on Multimedia Retrieval (ICMR), 2025
This paper proposes an adaptive hypergraph-based method for 3D multi-person pose estimation.
Recommended citation: X Cai, X Wang, K Qu, M Dai, Y Li. (2025). "Adaptive Hypergraph-Based 3D Multi-Person Pose Estimation Method for Intangible Cultural Heritage Dance Videos." Proceedings of the 2025 International Conference on Multimedia Retrieval, 35-42.
Published in European Conference on Computer Vision (ECCV), 2024
This paper presents DreamTexture, a method for high-fidelity synthetic 3D data generation.
Recommended citation: J Li, Y Luo, Y Li, X Li, X Li, Y Hao, L Wang, Z Li. (2024). "DreamTexture: High-Fidelity Synthetic 3D Data Generation Through Decoupled Geometry and Texture Synthesis." European Conference on Computer Vision, 303-320.
Published in 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA), 2018
Unveiling video streaming content from encrypted WiFi traffic using deep learning techniques.
Recommended citation: Y Li, Y Huang, R Xu, S Seneviratne, K Thilakarathna, A Cheng, D Webb, et al. (2018). "Deep content: Unveiling video streaming content from encrypted wifi traffic." 2018 IEEE 17th International Symposium on Network Computing and Applications, 1-8.