Staff profile

Conference Paper

• On Fine-tuned Deep Features for Unsupervised Domain Adaptation
  
  Wang, Q., Meng, F., & Breckon, T. (2023). On Fine-tuned Deep Features for Unsupervised Domain Adaptation. In IJCNN 2023 Conference Proceedings. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN54540.2023.10191262
• Source Class Selection with Label Propagation for Partial Domain Adaptation
  
  Wang, Q., & Breckon, T. (2021, September). Source Class Selection with Label Propagation for Partial Domain Adaptation. Presented at International Conference on Image Processing, Anchorage, AK.
• Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery
  
  Wang, Q., Bhowmik, N., & Breckon, T. (2021). Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery. Presented at 19th IEEE International Conference on Machine Learning and Applications (ICMLA 2020), Miami, Florida. https://doi.org/10.1109/icmla51294.2020.00012
• Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition
  
  Wang, Q., Bu, P., & Breckon, T. (2019). Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition. In 2019 International Joint Conference on Neural Networks (IJCNN). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ijcnn.2019.8852015
• A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks
  
  Wang, Q., Ning, J., & Breckon, T. (2019). A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks. In 2019 IEEE International Conference on Image Processing (ICIP) ; proceedings. (pp. 644-648). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/icip.2019.8803793

Journal Article

• Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders
  
  Wang, Q., & Breckon, T. (2023). Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders. Neural Networks, 163, 40-52. https://doi.org/10.1016/j.neunet.2023.03.033
• Data Augmentation with norm-VAE and Selective Pseudo-Labelling for Unsupervised Domain Adaptation
  
  Wang, Q., Meng, F., & Breckon, T. (2023). Data Augmentation with norm-VAE and Selective Pseudo-Labelling for Unsupervised Domain Adaptation. Neural Networks, 161, 614-625. https://doi.org/10.1016/j.neunet.2023.02.006
• Cross-Domain Structure Preserving Projection for Heterogeneous Domain Adaptation
  
  Wang, Q., & Breckon, T. (2022). Cross-Domain Structure Preserving Projection for Heterogeneous Domain Adaptation. Pattern Recognition, 123, Article 108362. https://doi.org/10.1016/j.patcog.2021.108362
• Digital Twins based Day-ahead Integrated Energy System Scheduling under Load and Renewable Energy Uncertainties
  
  You, M., Wang, Q., Sun, H., Castro, I., & Jiang, J. (2022). Digital Twins based Day-ahead Integrated Energy System Scheduling under Load and Renewable Energy Uncertainties. Applied Energy, 305, Article 117899. https://doi.org/10.1016/j.apenergy.2021.117899