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All Outputs (91)

Two-Person Interaction Augmentation with Skeleton Priors (2024)
Presentation / Conference Contribution
Li, B., Ho, E. S. L., Shum, H. P. H., & Wang, H. (2024, June). Two-Person Interaction Augmentation with Skeleton Priors. Presented at 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, Washington

Close and continuous interaction with rich contacts is a crucial aspect of human activities (e.g. hugging, dancing) and of interest in many domains like activity recognition, motion prediction, character animation, etc. However, acquiring such skelet... Read More about Two-Person Interaction Augmentation with Skeleton Priors.

Unraveling the Brain Dynamics of Depersonalization-Derealization Disorder: A Dynamic Functional Network Connectivity Analysis (2024)
Journal Article
Zheng, S., Zhang, F. X., Shum, H. P. H., Zhang, H., Song, N., Song, M., & Jia, H. (in press). Unraveling the Brain Dynamics of Depersonalization-Derealization Disorder: A Dynamic Functional Network Connectivity Analysis. BMC Psychiatry,

Background: Depersonalization-Derealization Disorder (DPD), a prevalent psychiatric disorder, fundamentally disrupts self-consciousness and could significantly impact the quality of life of those affected. While existing research has provided foundat... Read More about Unraveling the Brain Dynamics of Depersonalization-Derealization Disorder: A Dynamic Functional Network Connectivity Analysis.

Advancing healthcare practice and education via data sharing: demonstrating the utility of open data by training an artificial intelligence model to assess cardiopulmonary resuscitation skills. (2024)
Journal Article
Constable, M. D., Zhang, F. X., Conner, T., Monk, D., Rajsic, J., Ford, C., Park, L. J., Platt, A., Porteous, D., Grierson, L., & Shum, H. P. H. (online). Advancing healthcare practice and education via data sharing: demonstrating the utility of open data by training an artificial intelligence model to assess cardiopulmonary resuscitation skills. Advances in Health Sciences Education, https://doi.org/10.1007/s10459-024-10369-5

Health professional education stands to gain substantially from collective efforts toward building video databases of skill performances in both real and simulated settings. An accessible resource of videos that demonstrate an array of performances –... Read More about Advancing healthcare practice and education via data sharing: demonstrating the utility of open data by training an artificial intelligence model to assess cardiopulmonary resuscitation skills..

One-Index Vector Quantization Based Adversarial Attack on Image Classification (2024)
Journal Article
Fan, H., Qin, X., Chen, S., Shum, H. P. H., & Li, M. (2024). One-Index Vector Quantization Based Adversarial Attack on Image Classification. Pattern Recognition Letters, 186, 47-56. https://doi.org/10.1016/j.patrec.2024.09.001

To improve storage and transmission, images are generally compressed. Vector quantization (VQ) is a popular compression method as it has a high compression ratio that suppresses other compression techniques. Despite this, existing adversarial attack... Read More about One-Index Vector Quantization Based Adversarial Attack on Image Classification.

MxT: Mamba x Transformer for Image Inpainting (2024)
Presentation / Conference Contribution
Chen, S., Atapour-Abarghouei, A., Zhang, H., & Shum, H. P. H. (2024, November). MxT: Mamba x Transformer for Image Inpainting. Presented at BMVC 2024: The 35th British Machine Vision Conference, Glasgow, UK

Geometric Features Enhanced Human-Object Interaction Detection (2024)
Journal Article
Zhu, M., Ho, E. S. L., Chen, S., Yang, L., & Shum, H. P. H. (2024). Geometric Features Enhanced Human-Object Interaction Detection. IEEE Transactions on Instrumentation and Measurement, 73, Article 5026014. https://doi.org/10.1109/TIM.2024.3427800

Cameras are essential vision instruments to capture images for pattern detection and measurement. Human–object interaction (HOI) detection is one of the most popular pattern detection approaches for captured human-centric visual scenes. Recently, Tra... Read More about Geometric Features Enhanced Human-Object Interaction Detection.

From Category to Scenery: An End-to-End Framework for Multi-Person Human-Object Interaction Recognition in Videos (2024)
Presentation / Conference Contribution
Qiao, T., Li, R., Li, F. W. B., & Shum, H. P. H. (2024, December). From Category to Scenery: An End-to-End Framework for Multi-Person Human-Object Interaction Recognition in Videos. Presented at Proceedings of the 2024 International Conference on Pattern Recognition, Kolkata, India, 2024., Kolkata, India

Video-based Human-Object Interaction (HOI) recognition explores the intricate dynamics between humans and objects, which are essential for a comprehensive understanding of human behavior and intentions. While previous work has made significant stride... Read More about From Category to Scenery: An End-to-End Framework for Multi-Person Human-Object Interaction Recognition in Videos.

Self-Regulated Sample Diversity in Large Language Models (2024)
Presentation / Conference Contribution
Liu, M., Frawley, J., Wyer, S., Shum, H. P. H., Uckelman, S. L., Black, S., & Willcocks, C. G. (2024). Self-Regulated Sample Diversity in Large Language Models. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics (1891–1899)

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.

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). A Virtual Reality Framework for Human-Driver Interaction Research: Safe and Cost-Effective Data Collection. In HRI '24: Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (167-174). https://doi.org/10.1145/3610977.3634923

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.

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.