Skip to main content

Research Repository

Advanced Search

All Outputs (203)

Superpixel-based Anomaly Detection for Irregular Textures with a Focus on Pixel-level Accuracy (2024)
Presentation / Conference Contribution
Rafiei, M., Breckon, T. P., & Iosifidis, A. (2024, June). Superpixel-based Anomaly Detection for Irregular Textures with a Focus on Pixel-level Accuracy. Presented at 2024 International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan

Recent anomaly detection methods achieve high performance on commonly used image and pixel-level metrics. However, due to the imbalance in the number of normal and abnormal pixels commonly encountered in anomaly detection problems, commonly adopted p... Read More about Superpixel-based Anomaly Detection for Irregular Textures with a Focus on Pixel-level Accuracy.

U3DS3 : Unsupervised 3D Semantic Scene Segmentation (2024)
Presentation / Conference Contribution
Liu, J., Yu, Z., Breckon, T. P., & Shum, H. P. H. (2024). U3DS3 : Unsupervised 3D Semantic Scene Segmentation. In 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (3747-3756). https://doi.org/10.1109/WACV57701.2024.00372

Contemporary point cloud segmentation approaches largely rely on richly annotated 3D training data. However , it is both time-consuming and challenging to obtain consistently accurate annotations for such 3D scene data. Moreover, there is still a lac... Read More about U3DS3 : Unsupervised 3D Semantic Scene Segmentation.

Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics (2024)
Preprint / Working Paper
Yucer, S., Atapour-Abarghouei, A., Al Moubayed, N., & Breckon, T. P. Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics

Achieving an effective fine-grained appearance variation over 2D facial images, whilst preserving facial identity, is a challenging task due to the high complexity and entanglement of common 2D facial feature encoding spaces. Despite these challenges... Read More about Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics.

Progressively Select and Reject Pseudo-labelled Samples for Open-Set Domain Adaptation (2024)
Journal Article
Wang, Q., Meng, F., & Breckon, T. P. (2024). Progressively Select and Reject Pseudo-labelled Samples for Open-Set Domain Adaptation. IEEE Transactions on Artificial Intelligence, https://doi.org/10.1109/TAI.2024.3379940

Domain adaptation solves image classification problems in the target domain by taking advantage of the labelled source data and unlabelled target data. Usually, the source and target domains share the same set of classes. As a special case, Open-Set... Read More about Progressively Select and Reject Pseudo-labelled Samples for Open-Set Domain Adaptation.

Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption (2023)
Presentation / Conference Contribution
Barker, J., Bhowmik, N., Gaus, Y., & Breckon, T. (2023). Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption. . https://doi.org/10.5220/0011684700003417

Anomaly detection is the task of recognising novel samples which deviate significantly from pre-established normality. Abnormal classes are not present during training meaning that models must learn effective representations solely across normal clas... Read More about Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption.

Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers (2023)
Presentation / Conference Contribution
Corona-Figueroa, A., Bond-Taylor, S., Bhowmik, N., Gaus, Y. F. A., Breckon, T. P., Shum, H. P., & Willcocks, C. G. (2023). Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers. In ICCV '23: Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision. https://doi.org/10.1109/ICCV51070.2023.01341

Generating 3D images of complex objects conditionally from a few 2D views is a difficult synthesis problem, compounded by issues such as domain gap and geometric misalignment. For instance, a unified framework such as Generative Adversarial Networks... Read More about Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers.

Neural architecture search: A contemporary literature review for computer vision applications (2023)
Journal Article
Poyser, M., & Breckon, T. P. (2024). Neural architecture search: A contemporary literature review for computer vision applications. Pattern Recognition, 147, 110052. https://doi.org/10.1016/j.patcog.2023.110052

Deep Neural Networks have received considerable attention in recent years. As the complexity of network architecture increases in relation to the task complexity, it becomes harder to manually craft an optimal neural network architecture and train it... Read More about Neural architecture search: A contemporary literature review for computer vision applications.

Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation (2023)
Presentation / Conference Contribution
Li, L., Shum, H. P., & Breckon, T. P. (2023). Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR52729.2023.00903

Whilst the availability of 3D LiDAR point cloud data has significantly grown in recent years, annotation remains expensive and time-consuming, leading to a demand for semisupervised semantic segmentation methods with application domains such as auton... Read More about Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation.

ACR: Attention Collaboration-based Regressor for Arbitrary Two-Hand Reconstruction (2023)
Presentation / Conference Contribution
Yu, Z., Haung, S., Fang, C., Breckon, T., & Wang, J. (2023). ACR: Attention Collaboration-based Regressor for Arbitrary Two-Hand Reconstruction. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR52729.2023.01245

Reconstructing two hands from monocular RGB images is challenging due to frequent occlusion and mutual confusion. Existing methods mainly learn an entangled representation to encode two interacting hands, which are incredibly fragile to impaired inte... Read More about ACR: Attention Collaboration-based Regressor for Arbitrary Two-Hand Reconstruction.

Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening (2023)
Presentation / Conference Contribution
Issac-Medina, B., Yucer, S., Bhowmik, N., & Breckon, T. (2023). Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). https://doi.org/10.1109/CVPRW59228.2023.00059

The rapid progress in automatic prohibited object detection within the context of X-ray security screening, driven forward by advances in deep learning, has resulted in the first internationally-recognized, application-focused object detection perfor... Read More about Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening.

Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery (2023)
Presentation / Conference Contribution
Gaus, Y., Bhowmik, N., Issac-Medina, B., Atapour-Abarghouei, A., Shum, H., & Breckon, T. (2023). Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). https://doi.org/10.1109/CVPRW59228.2023.00301

Anomaly detection is a classical problem within automated visual surveillance, namely the determination of the normal from the abnormal when operational data availability is highly biased towards one class (normal) due to both insufficient sample siz... Read More about Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery.

On Fine-tuned Deep Features for Unsupervised Domain Adaptation (2023)
Presentation / Conference Contribution
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.

Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields (2023)
Presentation / Conference Contribution
Isaac-Medina, B., Willcocks, C., & Breckon, T. (2023). Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields.

Neural Radiance Fields (NeRF) have attracted significant attention due to their ability to synthesize novel scene views with great accuracy. However, inherent to their underlying formulation, the sampling of points along a ray with zero width may res... Read More about Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields.

Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders (2023)
Journal Article
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

Domain adaptation aims to exploit useful information from the source domain where annotated training data are easier to obtain to address a learning problem in the target domain where only limited or even no annotated data are available. In classific... Read More about Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders.

Data Augmentation with norm-VAE and Selective Pseudo-Labelling for Unsupervised Domain Adaptation (2023)
Journal Article
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

We address the Unsupervised Domain Adaptation (UDA) problem in image classification from a new perspective. In contrast to most existing works which either align the data distributions or learn domain-invariant features, we directly learn a unified c... Read More about Data Augmentation with norm-VAE and Selective Pseudo-Labelling for Unsupervised Domain Adaptation.