Outputs (105)

A Virtual Reality Framework for Human-Driver Interaction Research: Safe and Cost-Effective Data Collection (2024)
Presentation / Conference Contribution
Crosato, L., Wei, C., Ho, E. S. L., Shum, H. P. H., & Sun, Y. (2024, March). A Virtual Reality Framework for Human-Driver Interaction Research: Safe and Cost-Effective Data Collection. Presented at 2024 ACM/IEEE International Conference on Human Robot Interaction (HRI '24), Boulder, CO, USA

The advancement of automated driving technology has led to new challenges in the interaction between automated vehicles and human road users. However, there is currently no complete theory that explains how human road users interact with vehicles, an... Read More about A Virtual Reality Framework for Human-Driver Interaction Research: Safe and Cost-Effective Data Collection.

HINT: High-quality INpainting Transformer with Mask-Aware Encoding and Enhanced Attention (2024)
Journal Article
Chen, S., Atapour-Abarghouei, A., & Shum, H. P. H. (2024). HINT: High-quality INpainting Transformer with Mask-Aware Encoding and Enhanced Attention. IEEE Transactions on Multimedia, 26, 7649-7660. https://doi.org/10.1109/TMM.2024.3369897

Existing image inpainting methods leverage convolution-based downsampling approaches to reduce spatial dimensions. This may result in information loss from corrupted images where the available information is inherently sparse, especially for the scen... Read More about HINT: High-quality INpainting Transformer with Mask-Aware Encoding and Enhanced Attention.

Enhancing surgical performance in cardiothoracic surgery with innovations from computer vision and artificial intelligence: a narrative review (2024)
Journal Article
Constable, M. D., Shum, H. P. H., & Clark, S. (2024). Enhancing surgical performance in cardiothoracic surgery with innovations from computer vision and artificial intelligence: a narrative review. Journal of Cardiothoracic Surgery, 19(1), Article 94. https://doi.org/10.1186/s13019-024-02558-5

When technical requirements are high, and patient outcomes are critical, opportunities for monitoring and improving surgical skills via objective motion analysis feedback may be particularly beneficial. This narrative review synthesises work on techn... Read More about Enhancing surgical performance in cardiothoracic surgery with innovations from computer vision and artificial intelligence: a narrative review.

Pose-based tremor type and level analysis for Parkinson’s disease from video (2024)
Journal Article
Zhang, H., Ho, E. S. L., Zhang, X., Del Din, S., & Shum, H. P. H. (2024). Pose-based tremor type and level analysis for Parkinson’s disease from video. International Journal of Computer Assisted Radiology and Surgery, 19(5), 831-840. https://doi.org/10.1007/s11548-023-03052-4

Current methods for diagnosis of PD rely on clinical examination. The accuracy of diagnosis ranges between 73 and 84%, and is influenced by the experience of the clinical assessor. Hence, an automatic, effective and interpretable supporting system fo... Read More about Pose-based tremor type and level analysis for Parkinson’s disease from video.

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, October). Hard No-Box Adversarial Attack on Skeleton-Based Human Action Recognition with Skeleton-Motion-Informed Gradient. Presented at ICCV 2023: 2023 IEEE/CVF International Conference on Computer Vision (ICCV), Paris, France

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.

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.

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, October). Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers. Presented at ICCV23: 2023 IEEE/CVF International Conference on Computer Vision, Paris, France

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.

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.

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, October). Enhancing Perception and Immersion in Pre-Captured Environments through Learning-Based Eye Height Adaptation. Presented at ISMAR 23: International Symposium on Mixed and Augmented Reality, Sydney, Australia

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.

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, October). A Mixed Reality Training System for Hand-Object Interaction in Simulated Microgravity Environments. Presented at ISMAR 23: International Symposium on Mixed and Augmented Reality, Sydney, Australia

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.