Outputs (40)

FMDConv: Fast multi-attention dynamic convolution via speed-accuracy trade-off (2025)
Journal Article
Zhang, T., Wan, F., Duan, H., Tong, K. W., Deng, J., & Long, Y. (2025). FMDConv: Fast multi-attention dynamic convolution via speed-accuracy trade-off. Knowledge-Based Systems, 317, Article 113393. https://doi.org/10.1016/j.knosys.2025.113393

Spatial convolution is fundamental in constructing deep Convolutional Neural Networks (CNNs) for visual recognition. While dynamic convolution enhances model accuracy by adaptively combining static kernels, it incurs significant computational overhe... Read More about FMDConv: Fast multi-attention dynamic convolution via speed-accuracy trade-off.

Sparse representation for restoring images by exploiting topological structure of graph of patches (2025)
Journal Article
Gao, Y., Cai, Z., Xie, X., Deng, J., Dou, Z., & Ma, X. (2025). Sparse representation for restoring images by exploiting topological structure of graph of patches. IET Image Processing, 19(1), Article e70004. https://doi.org/10.1049/ipr2.70004

Image restoration poses a significant challenge, aiming to accurately recover damaged images by delving into their inherent characteristics. Various models and algorithms have been explored by researchers to address different types of image distortio... Read More about Sparse representation for restoring images by exploiting topological structure of graph of patches.

Artificial intelligence for geometry-based feature extraction, analysis and synthesis in artistic images: a survey (2024)
Journal Article
Vijendran, M., Deng, J., Chen, S., Ho, E. S. L., & Shum, H. P. H. (2025). Artificial intelligence for geometry-based feature extraction, analysis and synthesis in artistic images: a survey. Artificial Intelligence Review, 58(2), Article 64. https://doi.org/10.1007/s10462-024-11051-3

Artificial Intelligence significantly enhances the visual art industry by analyzing, identifying and generating digitized artistic images. This review highlights the substantial benefits of integrating geometric data into AI models, addressing challe... Read More about Artificial intelligence for geometry-based feature extraction, analysis and synthesis in artistic images: a survey.

Adaptive Graph Learning from Spatial Information for Surgical Workflow Anticipation (2024)
Journal Article
Zhang, F. X., Deng, J., Lieck, R., & Shum, H. P. (2025). Adaptive Graph Learning from Spatial Information for Surgical Workflow Anticipation. IEEE Transactions on Medical Robotics and Bionics, 7(1), 266-280. https://doi.org/10.1109/TMRB.2024.3517137

Surgical workflow anticipation is the task of predicting the timing of relevant surgical events from live video data, which is critical in Robotic-Assisted Surgery (RAS). Accurate predictions require the use of spatial information to model surgical i... Read More about Adaptive Graph Learning from Spatial Information for Surgical Workflow Anticipation.

Centersam: Fully Automatic Prompt for Dense Nucleus Segmentation (2024)
Presentation / Conference Contribution
Li, Y., Ren, H., Deng, J., Ma, X., & Xie, X. (2024, May). Centersam: Fully Automatic Prompt for Dense Nucleus Segmentation. Presented at 2024 IEEE International Symposium on Biomedical Imaging (ISBI), Athens, Greece

Nucleus segmentation is a vitally important task in biomedical image analysis which leads to multiple applications such as cellular behavior study, tumor detection and cancer diagnosis. However, challenges, such as ambiguous boundary for touching or... Read More about Centersam: Fully Automatic Prompt for Dense Nucleus Segmentation.

ST-SACLF: Style Transfer Informed Self-Attention Classifier for Bias-Aware Painting Classification (2024)
Book Chapter
Vijendran, M., Li, F. W. B., Deng, J., & Shum, H. P. H. (in press). ST-SACLF: Style Transfer Informed Self-Attention Classifier for Bias-Aware Painting Classification. In CCIS '24: Communications in Computer and Information Science. Springer

A survey on vulnerability of federated learning: A learning algorithm perspective (2024)
Journal Article
Xie, X., Hu, C., Ren, H., & Deng, J. (2024). A survey on vulnerability of federated learning: A learning algorithm perspective. Neurocomputing, 573, Article 127225. https://doi.org/10.1016/j.neucom.2023.127225

Federated Learning (FL) has emerged as a powerful paradigm for training Machine Learning (ML), particularly Deep Learning (DL) models on multiple devices or servers while maintaining data localized at owners’ sites. Without centrali... Read More about A survey on vulnerability of federated learning: A learning algorithm perspective.

An Element-Wise Weights Aggregation Method for Federated Learning (2023)
Presentation / Conference Contribution
Hu, Y., Ren, H., Hu, C., Deng, J., & Xie, X. (2023, December). An Element-Wise Weights Aggregation Method for Federated Learning. Presented at 2023 IEEE International Conference on Data Mining Workshops (ICDMW), Shanghai, China

Federated learning (FL) is a powerful Machine Learning (ML) paradigm that enables distributed clients to collaboratively learn a shared global model while keeping the data on the original device, thereby preserving privacy. A central challenge in FL... Read More about An Element-Wise Weights Aggregation Method for Federated Learning.

FedBoosting: Federated learning with gradient protected boosting for text recognition (2023)
Journal Article
Ren, H., Deng, J., Xie, X., Ma, X., & Wang, Y. (2024). FedBoosting: Federated learning with gradient protected boosting for text recognition. Neurocomputing, 569, Article 127126. https://doi.org/10.1016/j.neucom.2023.127126

Conventional machine learning methodologies require the centralization of data for model training, which may be infeasible in situations where data sharing limitations are imposed due to concerns such as privacy and gradient protect... Read More about FedBoosting: Federated learning with gradient protected boosting for text recognition.

Image restoration with group sparse representation and low‐rank group residual learning (2023)
Journal Article
Cai, Z., Xie, X., Deng, J., Dou, Z., Tong, B., & Ma, X. (2024). Image restoration with group sparse representation and low‐rank group residual learning. IET Image Processing, 18(3), 741-760. https://doi.org/10.1049/ipr2.12982

Image restoration, as a fundamental research topic of image processing, is to reconstruct the original image from degraded signal using the prior knowledge of image. Group sparse representation (GSR) is powerful for image restoration; it however ofte... Read More about Image restoration with group sparse representation and low‐rank group residual learning.