On Fine-tuned Deep Features for Unsupervised Domain Adaptation (2023)
Conference Proceeding
Wang, Q., Meng, F., & Breckon, T. (2023). On Fine-tuned Deep Features for Unsupervised Domain Adaptation. . https://doi.org/10.1109/IJCNN54540.2023.10191262

Prior feature transformation based approaches to Unsupervised Domain Adaptation (UDA) employ the deep features extracted by pre-trained deep models without fine-tuning them on the specific source or target domain data for a particular domain adaptati... Read More about On Fine-tuned Deep Features for Unsupervised Domain Adaptation.

Source Class Selection with Label Propagation for Partial Domain Adaptation (2021)
Conference Proceeding
Wang, Q., & Breckon, T. (2021). Source Class Selection with Label Propagation for Partial Domain Adaptation.

In traditional unsupervised domain adaptation problems, the target domain is assumed to share the same set of classes as the source domain. In practice, there exist situations where target-domain data are from only a subset of source-domain classes a... Read More about Source Class Selection with Label Propagation for Partial Domain Adaptation.

Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery (2021)
Conference Proceeding
Wang, Q., Bhowmik, N., & Breckon, T. (2021). Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery. . https://doi.org/10.1109/icmla51294.2020.00012

Automatic detection of prohibited objects within passenger baggage is important for aviation security. X-ray Computed Tomography (CT) based 3D imaging is widely used in airports for aviation security screening whilst prior work on automatic prohibite... Read More about Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery.

A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks (2019)
Conference Proceeding
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 (644-648). https://doi.org/10.1109/icip.2019.8803793

Recent studies on multi-label image classification have focused on designing more complex architectures of deep neural networks such as the use of attention mechanisms and region proposal networks. Although performance gains have been reported, the b... Read More about A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks.

Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition (2019)
Conference Proceeding
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). https://doi.org/10.1109/ijcnn.2019.8852015

Unsupervised domain adaptation aims to transfer knowledge from a source domain to a target domain so that the target domain data can be recognized without any explicit labelling information for this domain. One limitation of the problem setting is th... Read More about Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition.