Outputs (1463)

Unifying Human Motion Synthesis and Style Transfer with Denoising Diffusion Probabilistic Models (2023)
Presentation / Conference Contribution
Chang, Z., Findlay, E. J., Zhang, H., & Shum, H. P. (2023, February). Unifying Human Motion Synthesis and Style Transfer with Denoising Diffusion Probabilistic Models. Presented at GRAPP 2023: 2023 International Conference on Computer Graphics Theory and Applications, Lisbon, Portugal

Generating realistic motions for digital humans is a core but challenging part of computer animations and games, as human motions are both diverse in content and rich in styles. While the latest deep learning approaches have made significant advancem... Read More about Unifying Human Motion Synthesis and Style Transfer with Denoising Diffusion Probabilistic Models.

Tackling Data Bias in Painting Classification with Style Transfer (2023)
Presentation / Conference Contribution
Vijendran, M., Li, F. W., & Shum, H. P. (2023, February). Tackling Data Bias in Painting Classification with Style Transfer. Presented at VISAPP '23: 2023 International Conference on Computer Vision Theory and Applications, Lisbon, Portugal

It is difficult to train classifiers on paintings collections due to model bias from domain gaps and data bias from the uneven distribution of artistic styles. Previous techniques like data distillation, traditional data augmentation and style transf... Read More about Tackling Data Bias in Painting Classification with Style Transfer.

Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption (2023)
Presentation / Conference Contribution
Barker, J., Bhowmik, N., Gaus, Y., & Breckon, T. (2023, February). Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption. Presented at VISAPP 2023: 18th International Conference on Computer Vision Theory and Applications, Lisbon, Portugal

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.

Anomaly Detection with Transformers in Face Anti-spoofing (2023)
Presentation / Conference Contribution
Abduh, L., Omar, L., & Ivrissimtzis, I. (2023, May). Anomaly Detection with Transformers in Face Anti-spoofing. Presented at WSGC 2023: 31. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2023, Plzen, Czech Republic

Transformers are emerging as the new gold standard in various computer vision applications, and have already been used in face anti-spoofing demonstrating competitive performance. In this paper, we propose a network with the ViT transformer and ResNe... Read More about Anomaly Detection with Transformers in Face Anti-spoofing.

Precision in spatial working memory examined with mouse pointing (2023)
Journal Article
McAteer, S. M., McAteer, S. M., McGregor, A., Smith, D. T., & Smith, D. T. (2024). Precision in spatial working memory examined with mouse pointing. Vision Research, 215, Article 108343. https://doi.org/10.1016/j.visres.2023.108343

The capacity of visuospatial working memory (VSWM) is limited. However, there is continued debate surrounding the nature of this capacity limitation. The resource model (Bays et al., 2009) proposes that VSWM capacity is limited by the precision with... Read More about Precision in spatial working memory examined with mouse pointing.

A presaccadic perceptual impairment at the postsaccadic location of the blindspot (2023)
Journal Article
Smith, D. T., Beierholm, U., & Avery, M. (2023). A presaccadic perceptual impairment at the postsaccadic location of the blindspot. PLoS ONE, 18(9), Article e0291582. https://doi.org/10.1371/journal.pone.0291582

Saccadic eye movements are preceded by profound changes in visual perception. These changes have been linked to the phenomenon of ‘forward remapping’, in which cells begin to respond to stimuli that appear in their post-saccadic receptive field befor... Read More about A presaccadic perceptual impairment at the postsaccadic location of the blindspot.

Exploring the Potential of Immersive Virtual Environments for Learning American Sign Language (2023)
Presentation / Conference Contribution
Wang, J., Ivrissimtzis, I., Li, Z., Zhou, Y., & Shi, L. (2023, September). Exploring the Potential of Immersive Virtual Environments for Learning American Sign Language. Presented at ECTEL 2023: Eighteenth European Conference on Technology Enhanced Learning, Aveiro, Portugal

Sign languages enable effective communication between deaf and hearing people. Despite years of extensive pedagogical research, learning sign language online comes with a number of difficulties that might be frustrating for some students. Indeed, mos... Read More about Exploring the Potential of Immersive Virtual Environments for Learning American Sign Language.

Developing and Evaluating a Novel Gamified Virtual Learning Environment for ASL (2023)
Presentation / Conference Contribution
Wang, J., Ivrissimtzis, I., Li, Z., Zhou, Y., & Shi, L. (2023, August). Developing and Evaluating a Novel Gamified Virtual Learning Environment for ASL. Presented at INTERACT 2023: IFIP Conference on Human-Computer Interaction, York

The use of sign language is a highly effective way of communicating with individuals who experience hearing loss. Despite extensive research, many learners find traditional methods of learning sign language, such as web-based question-answer methods,... Read More about Developing and Evaluating a Novel Gamified Virtual Learning Environment for ASL.

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, June). Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation. Presented at 2023 IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), Vancouver, BC

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, June). ACR: Attention Collaboration-based Regressor for Arbitrary Two-Hand Reconstruction. Presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023, Vancouver, BC

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.

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, June). Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery. Presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023, Vancouver, BC

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.

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, June). Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening. Presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023, Vancouver, BC

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.

Different types of uncertainty in multisensory perceptual decision making (2023)
Journal Article
Aston, S., Nardini, M., & Beierholm, U. (2023). Different types of uncertainty in multisensory perceptual decision making. Philosophical Transactions of the Royal Society B: Biological Sciences, 378(1886), Article 20220349. https://doi.org/10.1098/rstb.2022.0349

Efficient decision-making requires accounting for sources of uncertainty (noise, or variability). Many studies have shown how the nervous system is able to account for perceptual uncertainty (noise, variability) that arises from limitations in its ow... Read More about Different types of uncertainty in multisensory perceptual decision making.

On Fine-tuned Deep Features for Unsupervised Domain Adaptation (2023)
Presentation / Conference Contribution
Wang, Q., Meng, F., & Breckon, T. (2023, June). On Fine-tuned Deep Features for Unsupervised Domain Adaptation. Presented at IJCNN 2023: International Joint Conference on Neural Networks, Queensland, Australia

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.

C2SPoint: A classification-to-saliency network for point cloud saliency detection (2023)
Journal Article
Jiang, Z., Ding, L., Tam, G., Song, C., Li, F. W., & Yang, B. (online). C2SPoint: A classification-to-saliency network for point cloud saliency detection. Computers and Graphics, 115, 274-284. https://doi.org/10.1016/j.cag.2023.07.003

Point cloud saliency detection is an important technique that support downstream tasks in 3D graphics and vision, like 3D model simplification, compression, reconstruction and viewpoint selection. Existing approaches often rely on hand-crafted featur... Read More about C2SPoint: A classification-to-saliency network for point cloud saliency detection.

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, June). Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields. Presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023, Vancouver, BC

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.

OrthopedVR: clinical assessment and pre-operative planning of paediatric patients with lower limb rotational abnormalities in virtual reality (2023)
Journal Article
Sibrina, D., Bethapudi, S., & Koulieris, G. A. (online). OrthopedVR: clinical assessment and pre-operative planning of paediatric patients with lower limb rotational abnormalities in virtual reality. Visual Computer, 39, 3621–3633. https://doi.org/10.1007/s00371-023-02949-0

Rotational abnormalities in the lower limbs causing patellar mal-tracking negatively affect patients’ lives, particularly young patients (10–17 years old). Recent studies suggest that rotational abnormalities can increase degenerative effects on the... Read More about OrthopedVR: clinical assessment and pre-operative planning of paediatric patients with lower limb rotational abnormalities in virtual reality.

Friendship habits questionnaire: A measure of group- versus dyadic-oriented socializing styles (2023)
Journal Article
Howlett, P., Baysu, G., Atkinson, A. P., Jungert, T., & Rychlowska, M. (2023). Friendship habits questionnaire: A measure of group- versus dyadic-oriented socializing styles. PLoS ONE, 18(6), Article e0285767. https://doi.org/10.1371/journal.pone.0285767

Friendships are central to our social lives, yet little is known about individual differences associated with the number of friends people enjoy spending time with. Here we present the Friendship Habits Questionnaire (FHQ), a new scale of group versu... Read More about Friendship habits questionnaire: A measure of group- versus dyadic-oriented socializing styles.

Bivariate non-uniform subdivision schemes based on L-systems (2023)
Journal Article
Gérot, C., & Ivrissimtzis, I. (2023). Bivariate non-uniform subdivision schemes based on L-systems. Applied Mathematics and Computation, 457, Article 128156. https://doi.org/10.1016/j.amc.2023.128156

L–systems have been used to describe non-uniform, univariate, subdivision schemes, which offer more flexible refinement processes than the uniform schemes, while at the same time are easier to analyse than the geometry driven non-uniform schemes. In... Read More about Bivariate non-uniform subdivision schemes based on L-systems.

Dichotic-listening performance after complete callosotomy: No relief from left-ear extinction by selective attention (2023)
Journal Article
Westerhausen, R., Fabri, M., & Hausmann, M. (2023). Dichotic-listening performance after complete callosotomy: No relief from left-ear extinction by selective attention. Neuropsychologia, 188, Article 108627. https://doi.org/10.1016/j.neuropsychologia.2023.108627

The surgical section of the corpus callosum (callosotomy) has been frequently demonstrated to result in a left-ear extinction in dichotic listening. That is, callosotomy patients report the left-ear stimulus below chance level, resulting in substanti... Read More about Dichotic-listening performance after complete callosotomy: No relief from left-ear extinction by selective attention.