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Dr Frederick Li's Outputs (74)

Failure rates in introductory programming revisited
Presentation / Conference Contribution
Watson, C., & Li, F. W. (2014, December). Failure rates in introductory programming revisited. Presented at 2014 conference on Innovation & technology in computer science education (ITiCSE), Uppsala

Whilst working on an upcoming meta-analysis that synthesized fifty years of research on predictors of programming performance, we made an interesting discovery. Despite several studies citing a motivation for research as the high failure rates of int... Read More about Failure rates in introductory programming revisited.

Predicting Performance in an Introductory Programming Course by Logging and Analyzing Student Programming Behavior
Presentation / Conference Contribution
Watson, C., Li, F. W., & Godwin, J. L. (2013, December). Predicting Performance in an Introductory Programming Course by Logging and Analyzing Student Programming Behavior. Presented at 2013 IEEE 13th International Conference on Advanced Learning Technologies, Beijing

The high failure rates of many programming courses means there is a need to identify struggling students as early as possible. Prior research has focused upon using a set of tests to assess the use of a student's demographic, psychological and cognit... Read More about Predicting Performance in an Introductory Programming Course by Logging and Analyzing Student Programming Behavior.

No Tests Required: Comparing Traditional and Dynamic Predictors of Programming Success
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.

BlueFix: Using Crowd-sourced Feedback to Support Programming Students in Error Diagnosis and Repair
Presentation / Conference Contribution
Watson, C., Li, F. W., & Godwin, J. L. (2012, December). BlueFix: Using Crowd-sourced Feedback to Support Programming Students in Error Diagnosis and Repair. Presented at 11th International Conference on Advances in Web-Based Learning, Sinaia

Feedback is regarded as one of the most important influences on student learning and motivation. But standard compiler feedback is designed for experts - not novice programming students, who can find it difficult to interpret and understand. In this... Read More about BlueFix: Using Crowd-sourced Feedback to Support Programming Students in Error Diagnosis and Repair.

Modeling Detailed Cloud Scene from Multi-source Images
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.

Deep Blind Synthesized Image Quality Assessment with Contextual Multi-Level Feature Pooling
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.

Recognising Human-Object Interactions Using Attention-based LSTMs
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.

Image recoloring for home scene
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.

STGAE: Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising
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.

Geometric Features Informed Multi-person Human-object Interaction Recognition in Videos
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.

STIT: Spatio-Temporal Interaction Transformers for Human-Object Interaction Recognition in Videos
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.

Aesthetic Enhancement via Color Area and Location Awareness
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.

Tackling Data Bias in Painting Classification with Style Transfer
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.