Outputs (4)

BOOST: Out-of-distribution-informed adaptive sampling for bias mitigation in stylistic convolutional neural networks (2025)
Journal Article
Vijendran, M., Chen, S., Deng, J., & Shum, H. P. H. (2026). BOOST: Out-of-distribution-informed adaptive sampling for bias mitigation in stylistic convolutional neural networks. Expert Systems with Applications, 296(Part A), Article 128905. https://doi.org/10.1016/j.eswa.2025.128905

The pervasive issue of bias in AI presents a significant challenge to painting classification, and is getting more serious as these systems become increasingly integrated into tasks like art curation and restoration. Biases, often arising from imbala... Read More about BOOST: Out-of-distribution-informed adaptive sampling for bias mitigation in stylistic convolutional neural networks.

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.

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

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.