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Professor Hubert Shum's Outputs (17)

UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-Identification in Video Imagery (2022)
Presentation / Conference Contribution
Organisciak, D., Poyser, M., Alsehaim, A., Hu, S., Isaac-Medina, B. K., Breckon, T. P., & Shum, H. P. UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-Identification in Video Imagery. Presented at 2022 17th International Conference on Computer Vision Theory and Applications

As unmanned aerial vehicles (UAV) become more accessible with a growing range of applications, the risk of UAV disruption increases. Recent development in deep learning allows vision-based counter-UAV systems to detect and track UAVs with a single ca... Read More about UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-Identification in Video Imagery.

Denoising Diffusion Probabilistic Models for Styled Walking Synthesis (2022)
Presentation / Conference Contribution
Findlay, E., Zhang, H., Chang, Z., & Shum, H. P. (2022, November). Denoising Diffusion Probabilistic Models for Styled Walking Synthesis. Presented at MIG 2022: The 15th Annual ACM SIGGRAPH Conference on Motion, Interaction and Games, Guanajuato, Mexico

Generating realistic motions for digital humans is time-consuming for many graphics applications. Data-driven motion synthesis approaches have seen solid progress in recent years through deep generative models. These results offer high-quality motion... Read More about Denoising Diffusion Probabilistic Models for Styled Walking Synthesis.

A Skeleton-aware Graph Convolutional Network for Human-Object Interaction Detection (2022)
Presentation / Conference Contribution
Zhu, M., Ho, E. S., & Shum, H. P. (2022, October). A Skeleton-aware Graph Convolutional Network for Human-Object Interaction Detection. Presented at IEEE SMC 2022: International Conference on Systems, Man, and Cybernetics, Prague, Czech Republic

Detecting human-object interactions is essential for comprehensive understanding of visual scenes. In particular, spatial connections between humans and objects are important cues for reasoning interactions. To this end, we propose a skeleton-aware g... Read More about A Skeleton-aware Graph Convolutional Network for Human-Object Interaction Detection.

Towards Graph Representation Learning Based Surgical Workflow Anticipation (2022)
Presentation / Conference Contribution
Zhang, X., Al Moubayed, N., & Shum, H. P. (2022, September). Towards Graph Representation Learning Based Surgical Workflow Anticipation. Presented at 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Ioannina, Greece

Surgical workflow anticipation can give predictions on what steps to conduct or what instruments to use next, which is an essential part of the computer-assisted intervention system for surgery, e.g. workflow reasoning in robotic surgery. However, cu... Read More about Towards Graph Representation Learning Based Surgical Workflow Anticipation.

A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip (2022)
Presentation / Conference Contribution
Chen, S., Atapour-Abarghouei, A., Kerby, J., Ho, E. S., Sainsbury, D. C., Butterworth, S., & Shum, H. P. (2022, September). A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip. Presented at 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Ioannina, Greece

A Cleft lip is a congenital abnormality requiring surgical repair by a specialist. The surgeon must have extensive experience and theoretical knowledge to perform surgery, and Artificial Intelligence (AI) method has been proposed to guide surgeons in... Read More about A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip.

Geometric Features Informed Multi-person Human-object Interaction Recognition in Videos (2022)
Presentation / Conference Contribution
Qiao, T., Men, Q., Li, F. W., Kubotani, Y., Morishima, S., & Shum, H. P. (2022, October). Geometric Features Informed Multi-person Human-object Interaction Recognition in Videos. Presented at Computer Vision - ECCV 2022, Tel Aviv, Israel

Human-Object Interaction (HOI) recognition in videos is important for analysing human activity. Most existing work focusing on visual features usually suffer from occlusion in the real-world scenarios. Such a problem will be further complicated when... Read More about Geometric Features Informed Multi-person Human-object Interaction Recognition in Videos.

Multiclass-SGCN: Sparse Graph-based Trajectory Prediction with Agent Class Embedding (2022)
Presentation / Conference Contribution
Li, R., Katsigiannis, S., & Shum, H. P. (2022, October). Multiclass-SGCN: Sparse Graph-based Trajectory Prediction with Agent Class Embedding. Presented at ICIP 2022: IEEE International Conference in Image Processing, Bordeaux, France

Trajectory prediction of road users in real-world scenarios is challenging because their movement patterns are stochastic and complex. Previous pedestrian-oriented works have been successful in modelling the complex interactions among pedestrians, bu... Read More about Multiclass-SGCN: Sparse Graph-based Trajectory Prediction with Agent Class Embedding.

A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders Prediction (2022)
Journal Article
Zhu, M., Men, Q., Ho, E. S., Leung, H., & Shum, H. P. (2022). A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders Prediction. Journal of Medical Systems, 46(11), Article 76. https://doi.org/10.1007/s10916-022-01857-5

Musculoskeletal and neurological disorders are the most common causes of walking problems among older people, and they often lead to diminished quality of life. Analyzing walking motion data manually requires trained professionals and the evaluations... Read More about A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders Prediction.

CP-AGCN: Pytorch-based Attention Informed Graph Convolutional Network for Identifying Infants at Risk of Cerebral Palsy (2022)
Journal Article
Zhang, H., Ho, E. S., & Shum, H. P. (2022). CP-AGCN: Pytorch-based Attention Informed Graph Convolutional Network for Identifying Infants at Risk of Cerebral Palsy. Software impacts, 14, Article 100419. https://doi.org/10.1016/j.simpa.2022.100419

Early prediction is clinically considered one of the essential parts of cerebral palsy (CP) treatment. We propose to implement a low-cost and interpretable classification system for supporting CP prediction based on General Movement Assessment (GMA).... Read More about CP-AGCN: Pytorch-based Attention Informed Graph Convolutional Network for Identifying Infants at Risk of Cerebral Palsy.

Pose-based Tremor Classification for Parkinson’s Disease Diagnosis from Video (2022)
Presentation / Conference Contribution
Zhang, X., Zhang, H., & Shum, H. P. (2022, September). Pose-based Tremor Classification for Parkinson’s Disease Diagnosis from Video. Presented at MICCAI '22: The 25th International Conference on Medical Image Computing and Computer Assisted Intervention, Singapore

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that results in a variety of motor dysfunction symptoms, including tremors, bradykinesia, rigidity and postural instability. The diagnosis of PD mainly relies on clinical experience... Read More about Pose-based Tremor Classification for Parkinson’s Disease Diagnosis from Video.

MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray (2022)
Presentation / Conference Contribution
Corona-Figueroa, A., Frawley, J., Bond-Taylor, S., Bethapudi, S., Shum, H. P., & Willcocks, C. G. (2022, July). MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray. Presented at 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, Scotland

Computed tomography (CT) is an effective med-ical imaging modality, widely used in the field of clinical medicine for the diagnosis of various pathologies. Advances in Multidetector CT imaging technology have enabled additional functionalities, inclu... Read More about MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray.

Cerebral Palsy Prediction with Frequency Attention Informed Graph Convolutional Networks (2022)
Presentation / Conference Contribution
Zhang, H., Shum, H. P., & Ho, E. S. (2022, July). Cerebral Palsy Prediction with Frequency Attention Informed Graph Convolutional Networks. Presented at 2022 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Glasgow

Early diagnosis and intervention are clinically considered the paramount part of treating cerebral palsy (CP), so it is essential to design an efficient and interpretable automatic prediction system for CP. We highlight a significant difference betwe... Read More about Cerebral Palsy Prediction with Frequency Attention Informed Graph Convolutional Networks.

Interaction-aware Decision-making for Automated Vehicles using Social Value Orientation (2022)
Journal Article
Crosato, L., Shum, H. P., Ho, E. S., & Wei, C. (2023). Interaction-aware Decision-making for Automated Vehicles using Social Value Orientation. IEEE Transactions on Intelligent Vehicles, 8(2), 1339-1349. https://doi.org/10.1109/tiv.2022.3189836

Motion control algorithms in the presence of pedestrians are critical for the development of safe and reliable Autonomous Vehicles (AVs). Traditional motion control algorithms rely on manually designed decision-making policies which neglect the mutua... Read More about Interaction-aware Decision-making for Automated Vehicles using Social Value Orientation.

Formation Control for UAVs Using a Flux Guided Approach (2022)
Journal Article
Hartley, J., Shum, H. P., Ho, E. S., Wang, H., & Ramamoorthyd, S. (2022). Formation Control for UAVs Using a Flux Guided Approach. Expert Systems with Applications, 205, Article 117665. https://doi.org/10.1016/j.eswa.2022.117665

Existing studies on formation control for unmanned aerial vehicles (UAV) have not considered encircling targets where an optimum coverage of the target is required at all times. Such coverage plays a critical role in many real-world applications such... Read More about Formation Control for UAVs Using a Flux Guided Approach.

RobIn: A Robust Interpretable Deep Network for Schizophrenia Diagnosis (2022)
Journal Article
Organisciak, D., Shum, H. P., Nwoye, E., & Woo, W. L. (2022). RobIn: A Robust Interpretable Deep Network for Schizophrenia Diagnosis. Expert Systems with Applications, 201, Article 117158. https://doi.org/10.1016/j.eswa.2022.117158

Schizophrenia is a severe mental health condition that requires a long and complicated diagnostic process. However, early diagnosis is vital to control symptoms. Deep learning has recently become a popular way to analyse and interpret medical data. P... Read More about RobIn: A Robust Interpretable Deep Network for Schizophrenia Diagnosis.

360 Depth Estimation in the Wild - The Depth360 Dataset and the SegFuse Network (2022)
Presentation / Conference Contribution
Feng, Q., Shum, H. P., & Morishima, S. (2022, March). 360 Depth Estimation in the Wild - The Depth360 Dataset and the SegFuse Network. Presented at IEEE Conference on Virtual Reality and 3D User Interfaces, Christchurch, New Zealand

Single-view depth estimation from omnidirectional images has gained popularity with its wide range of applications such as autonomous driving and scene reconstruction. Although data-driven learning-based methods demonstrate significant potential in t... Read More about 360 Depth Estimation in the Wild - The Depth360 Dataset and the SegFuse Network.