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Dr Frederick Li's Outputs (27)

Multi-modal Dynamic Point Cloud Geometric Compression Based on Bidirectional Recurrent Scene Flow* (2025)
Presentation / Conference Contribution
Nan, F., Li, F., Wang, Z., Tam, G. K. L., Jiang, Z., DongZheng, D., & Yang, B. (2025, April). Multi-modal Dynamic Point Cloud Geometric Compression Based on Bidirectional Recurrent Scene Flow*. Presented at ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hyderabad, India

Deep learning methods have recently shown significant promise in compressing the geometric features of point clouds. However, challenges arise when consecutive point clouds contain holes, resulting in incomplete information that complicates motion es... Read More about Multi-modal Dynamic Point Cloud Geometric Compression Based on Bidirectional Recurrent Scene Flow*.

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 ICPR 2024: International Conference on Pattern Recognition, 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.

MAGR: Manifold-Aligned Graph Regularization for Continual Action Quality Assessment (2024)
Presentation / Conference Contribution
Zhou, K., Wang, L., Zhang, X., Shum, H. P., Li, F. W. B., Li, J., & Liang, X. (2024, September). MAGR: Manifold-Aligned Graph Regularization for Continual Action Quality Assessment. Presented at Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Milan, Italy

Action Quality Assessment (AQA) evaluates diverse skills but models struggle with non-stationary data. We propose Continual AQA (CAQA) to refine models using sparse new data. Feature replay preserves memory without storing raw inputs. However, the mi... Read More about MAGR: Manifold-Aligned Graph Regularization for Continual Action Quality Assessment.

Reducing University Students’ Exam Anxiety via Mindfulness-Based Cognitive Therapy in VR with Real-Time EEG Neurofeedback (2024)
Presentation / Conference Contribution
Pan, Z., Cristea, A. I., & Li, F. W. B. (2024, July). Reducing University Students’ Exam Anxiety via Mindfulness-Based Cognitive Therapy in VR with Real-Time EEG Neurofeedback. Presented at AIED 2024: Artificial Intelligence in Education, Recife, Brazil

This research aims to develop and evaluate a novel approach to reduce university students’ exam anxiety and teach them how to better manage it using a personalised, emotion-informed Mindfulness-Based Cognitive Therapy (MBCT) method, delivered within... Read More about Reducing University Students’ Exam Anxiety via Mindfulness-Based Cognitive Therapy in VR with Real-Time EEG Neurofeedback.

HSE: Hybrid Species Embedding for Deep Metric Learning (2024)
Presentation / Conference Contribution
Yang, B., Sun, H., Li, F. W. B., Chen, Z., Cai, J., & Song, C. (2023, October). HSE: Hybrid Species Embedding for Deep Metric Learning. Presented at 2023 IEEE/CVF International Conference on Computer Vision (ICCV), Paris

Deep metric learning is crucial for finding an embedding function that can generalize to training and testing data, including unknown test classes. However, limited training samples restrict the model's generalization to downstream tasks. While addin... Read More about HSE: Hybrid Species Embedding for Deep Metric Learning.

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.

Fg-T2M: Fine-Grained Text-Driven Human Motion Generation via Diffusion Model (2023)
Presentation / Conference Contribution
Wang, Y., Leng, Z., Li, F. W. B., Wu, S.-C., & Liang, X. (2023, October). Fg-T2M: Fine-Grained Text-Driven Human Motion Generation via Diffusion Model. Presented at 2023 IEEE/CVF International Conference on Computer Vision (ICCV), Paris

Text-driven human motion generation in computer vision is both significant and challenging. However, current methods are limited to producing either deterministic or imprecise motion sequences, failing to effectively control the temporal and spatial... Read More about Fg-T2M: Fine-Grained Text-Driven Human Motion Generation via Diffusion Model.

DrawGAN: Multi-view Generative Model Inspired By The Artist's Drawing Method (2023)
Presentation / Conference Contribution
Yang, B., Chen, Z., Li, F. W. B., Sun, H., & Cai, J. (2023, August). DrawGAN: Multi-view Generative Model Inspired By The Artist's Drawing Method. Presented at CGI 2023: Advances in Computer Graphics, Shanghai, China

We present a novel approach for modeling artists' drawing processes using an architecture that combines an unconditional generative adversarial network (GAN) with a multi-view generator and multi-discriminator. Our method excels in synthesizing vario... Read More about DrawGAN: Multi-view Generative Model Inspired By The Artist's Drawing Method.

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.

Gamifying Experiential Learning Theory (2022)
Presentation / Conference Contribution
Alsaqqaf, A., & Li, F. W. (2022, December). Gamifying Experiential Learning Theory. Presented at International Conference On Web-Based Learning (ICWL 2022), Tenerife, Spain

Aesthetic Enhancement via Color Area and Location Awareness (2022)
Presentation / Conference Contribution
Yang, B., Wang, Q., Li, F. W., Liang, X., Wei, T., & Zhu, C. (2022, October). Aesthetic Enhancement via Color Area and Location Awareness. Presented at The 30th Pacific Conference on Computer Graphics and Applications, Pacific Graphics 2022, Kyoto, Japan

Choosing a suitable color palette can typically improve image aesthetic, where a naive way is choosing harmonious colors from some pre-defined color combinations in color wheels. However, color palettes only consider the usage of color types without... Read More about Aesthetic Enhancement via Color Area and Location Awareness.

STIT: Spatio-Temporal Interaction Transformers for Human-Object Interaction Recognition in Videos (2022)
Presentation / Conference Contribution
Almushyti, M., & Li, F. W. (2022, August). STIT: Spatio-Temporal Interaction Transformers for Human-Object Interaction Recognition in Videos. Presented at 2022 26th International Conference on Pattern Recognition (ICPR), Montréal, Québec

Recognizing human-object interactions is challenging due to their spatio-temporal changes. We propose the SpatioTemporal Interaction Transformer-based (STIT) network to reason such changes. Specifically, spatial transformers learn humans and objects... Read More about STIT: Spatio-Temporal Interaction Transformers for Human-Object Interaction Recognition in Videos.

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.

STGAE: Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising (2021)
Presentation / Conference Contribution
Zhou, K., Cheng, Z., Shum, H. P., Li, F. W., & Liang, X. (2021, October). STGAE: Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising. Presented at 2021 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Bari, Italy

Hand object interaction in mixed reality (MR) relies on the accurate tracking and estimation of human hands, which provide users with a sense of immersion. However, raw captured hand motion data always contains errors such as joints occlusion, disloc... Read More about STGAE: Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising.

Recognising Human-Object Interactions Using Attention-based LSTMs (2019)
Presentation / Conference Contribution
Almushyti, M., & Li, F. W. (2019, December). Recognising Human-Object Interactions Using Attention-based LSTMs. Presented at Computer Graphics and Visual Computing (CGVC), Bangor University, United Kingdom

Recognising Human-object interactions (HOIs) in videos is a challenge task especially when a human can interact with multiple objects. This paper attempts to solve the problem of HOIs by proposing a hierarchical framework that analyzes human-object i... Read More about Recognising Human-Object Interactions Using Attention-based LSTMs.

Deep Blind Synthesized Image Quality Assessment with Contextual Multi-Level Feature Pooling (2019)
Presentation / Conference Contribution
Wang, X., Wang, K., Yang, B., Li, F. W., & Liang, X. (2019, December). Deep Blind Synthesized Image Quality Assessment with Contextual Multi-Level Feature Pooling. Presented at 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan

Blind image quality metrics have achieved significant improvement on traditional 2D image dataset, yet still being insufficient for evaluating synthesized images generated from depth-image-based rendering. The geometric distortions in synthesized ima... Read More about Deep Blind Synthesized Image Quality Assessment with Contextual Multi-Level Feature Pooling.

Image recoloring for home scene (2018)
Presentation / Conference Contribution
Lin, X., Wang, X., Li, F. W., Yang, B., Zhang, K., & Wei, T. (2018, December). Image recoloring for home scene. Presented at ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry (VRCAI '18), Tokyo, Japan

Indoor home scene coloring technology is a hot topic for home design, helping users make home coloring decisions. Image based home scene coloring is preferable for e-commerce customers since it only requires users to describe coloring expectations or... Read More about Image recoloring for home scene.

Modeling Detailed Cloud Scene from Multi-source Images (2018)
Presentation / Conference Contribution
Cen, Y., Liang, X., Chen, J., Yang, B., & Li, F. W. (2018, December). Modeling Detailed Cloud Scene from Multi-source Images. Presented at Pacific Graphics, Hong Kong

Realistic cloud is essential for enhancing the quality of computer graphics applications, such as flight simulation. Data-driven method is an effective way in cloud modeling, but existing methods typically only utilize one data source as input. For e... Read More about Modeling Detailed Cloud Scene from Multi-source Images.

No Tests Required: Comparing Traditional and Dynamic Predictors of Programming Success (2014)
Presentation / Conference Contribution
Watson, C., Li, F. W., & Godwin, J. L. (2014, December). No Tests Required: Comparing Traditional and Dynamic Predictors of Programming Success. Presented at 45th ACM Technical Symposium on Computer Science Education (SIGCSE '14), Atlanta GA

Research over the past fifty years into predictors of programming performance has yielded little improvement in the identification of at-risk students. This is possibly because research to date is based upon using static tests, which fail to reflect... Read More about No Tests Required: Comparing Traditional and Dynamic Predictors of Programming Success.