Professor Hubert Shum's Outputs (15)

Tackling Data Bias in Painting Classification with Style Transfer (2023)
Presentation / Conference Contribution
Vijendran, M., Li, F. W., & Shum, H. P. (2023). Tackling Data Bias in Painting Classification with Style Transfer. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 VISAPP: VISAPP (250-261). https://doi.org/10.5220/0011776600003417

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.

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). Unifying Human Motion Synthesis and Style Transfer with Denoising Diffusion Probabilistic Models. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - GRAPP (64-74). https://doi.org/10.5220/0011631000003417

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.

Hard No-Box Adversarial Attack on Skeleton-Based Human Action Recognition with Skeleton-Motion-Informed Gradient (2023)
Presentation / Conference Contribution
Lu, Z., Wang, H., Chang, Z., Yang, G., & Shum, H. P. (2023). Hard No-Box Adversarial Attack on Skeleton-Based Human Action Recognition with Skeleton-Motion-Informed Gradient. . https://doi.org/10.1109/ICCV51070.2023.00424

Recently, methods for skeleton-based human activity recognition have been shown to be vulnerable to adversarial attacks. However, these attack methods require either the full knowledge of the victim (i.e. white-box attacks), access to training data (... Read More about Hard No-Box Adversarial Attack on Skeleton-Based Human Action Recognition with Skeleton-Motion-Informed Gradient.

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.

A Mixed Reality Training System for Hand-Object Interaction in Simulated Microgravity Environments (2023)
Presentation / Conference Contribution
Zhou, K., Chen, C., Ma, Y., Leng, Z., Shum, H. P., Li, F. W., & Liang, X. (2023). A Mixed Reality Training System for Hand-Object Interaction in Simulated Microgravity Environments. In 2023 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). https://doi.org/10.1109/ISMAR59233.2023.00031

As human exploration of space continues to progress, the use of Mixed Reality (MR) for simulating microgravity environments and facilitating training in hand-object interaction holds immense practical significance. However, hand-object interaction in... Read More about A Mixed Reality Training System for Hand-Object Interaction in Simulated Microgravity Environments.

Enhancing Perception and Immersion in Pre-Captured Environments through Learning-Based Eye Height Adaptation (2023)
Presentation / Conference Contribution
Feng, Q., Shum, H. P., & Morishima, S. (2023). Enhancing Perception and Immersion in Pre-Captured Environments through Learning-Based Eye Height Adaptation. In 2023 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). https://doi.org/10.1109/ISMAR59233.2023.00055

Pre-captured immersive environments using omnidirectional cameras provide a wide range of virtual reality applications. Previous research has shown that manipulating the eye height in egocentric virtual environments can significantly affect distance... Read More about Enhancing Perception and Immersion in Pre-Captured Environments through Learning-Based Eye Height Adaptation.

Social Interaction‐Aware Dynamical Models and Decision‐Making for Autonomous Vehicles (2023)
Journal Article
Crosato, L., Tian, K., Shum, H. P., Ho, E. S., Wang, Y., & Wei, C. (2023). Social Interaction‐Aware Dynamical Models and Decision‐Making for Autonomous Vehicles. Advanced Intelligent Systems, 6(3), Article 2300575. https://doi.org/10.1002/aisy.202300575

Interaction‐aware autonomous driving (IAAD) is a rapidly growing field of research that focuses on the development of autonomous vehicles (AVs) that are capable of interacting safely and efficiently with human road users. This is a challenging task,... Read More about Social Interaction‐Aware Dynamical Models and Decision‐Making for Autonomous Vehicles.

Multi-Task Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising (2023)
Journal Article
Zhou, K., Shum, H. P., Li, F. W., & Liang, X. (2023). Multi-Task Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising. IEEE Transactions on Visualization and Computer Graphics, https://doi.org/10.1109/TVCG.2023.3337868

In many human-computer interaction applications, fast and accurate hand tracking is necessary for an immersive experience. However, raw hand motion data can be flawed due to issues such as joint occlusions and high-frequency noise, hindering the inte... Read More about Multi-Task Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising.

Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRI (2023)
Presentation / Conference Contribution
Zhang, X., Zheng, S., Shum, H. P., Zhang, H., Song, N., Song, M., & Jia, H. (2023). Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRI. In Neural Information Processing 30th International Conference, ICONIP 2023, Changsha, China, November 20–23, 2023, Proceedings, Part IX (298-312). https://doi.org/10.1007/978-981-99-8138-0_24

Resting-state fMRI (rs-fMRI) functional connectivity (FC)
analysis provides valuable insights into the relationships between different brain regions and their potential implications for neurological or psychiatric disorders. However, specific design... Read More about Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRI.

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.

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.

Hierarchical Graph Convolutional Networks for Action Quality Assessment (2023)
Journal Article
Zhou, K., Ma, Y., Shum, H. P., & Liang, X. (online). Hierarchical Graph Convolutional Networks for Action Quality Assessment. IEEE Transactions on Circuits and Systems for Video Technology, 33(12), 7749 - 7763. https://doi.org/10.1109/TCSVT.2023.3281413

Action quality assessment (AQA) automatically evaluates how well humans perform actions in a given video, a technique widely used in fields such as rehabilitation medicine, athletic competitions, and specific skills assessment. However, existing work... Read More about Hierarchical Graph Convolutional Networks for Action Quality Assessment.

INCLG: Inpainting for Non-Cleft Lip Generation with a Multi-Task Image Processing Network (2023)
Journal Article
Chen, S., Atapour-Abarghouei, A., Ho, E. S., & Shum, H. P. (2023). INCLG: Inpainting for Non-Cleft Lip Generation with a Multi-Task Image Processing Network. Software impacts, 17, Article 100517. https://doi.org/10.1016/j.simpa.2023.100517

We present a software that predicts non-cleft facial images for patients with cleft lip, thereby facilitating the understanding, awareness and discussion of cleft lip surgeries. To protect patients’ privacy, we design a software framework using image... Read More about INCLG: Inpainting for Non-Cleft Lip Generation with a Multi-Task Image Processing Network.

Focalized Contrastive View-invariant Learning for Self-supervised Skeleton-based Action Recognition (2023)
Journal Article
Men, Q., Ho, E. S., Shum, H. P., & Leung, H. (2023). Focalized Contrastive View-invariant Learning for Self-supervised Skeleton-based Action Recognition. Neurocomputing, 537, 198-209. https://doi.org/10.1016/j.neucom.2023.03.070

Learning view-invariant representation is a key to improving feature discrimination power for skeleton-based action recognition. Existing approaches cannot effectively remove the impact of viewpoint due to the implicit view-dependent representations.... Read More about Focalized Contrastive View-invariant Learning for Self-supervised Skeleton-based Action Recognition.

A Video-Based Augmented Reality System for Human-in-the-Loop Muscle Strength Assessment of Juvenile Dermatomyositis (2023)
Journal Article
Zhou, K., Cai, R., Ma, Y., Tan, Q., Wang, X., Li, J., …Liang, X. (2023). A Video-Based Augmented Reality System for Human-in-the-Loop Muscle Strength Assessment of Juvenile Dermatomyositis. IEEE Transactions on Visualization and Computer Graphics, 29(5), 2456-2466. https://doi.org/10.1109/tvcg.2023.3247092

As the most common idiopathic inflammatory myopathy in children, juvenile dermatomyositis (JDM) is characterized by skin rashes and muscle weakness. The childhood myositis assessment scale (CMAS) is commonly used to measure the degree of muscle invol... Read More about A Video-Based Augmented Reality System for Human-in-the-Loop Muscle Strength Assessment of Juvenile Dermatomyositis.