5 papers
MDTD-ArtIR: Benchmarking Image Editing and Restoration Models for Art Image Restoration under Texture-Overlay Degradations
Mridula Vijendran, Shuang Chen, Hubert P. H. Shum
Restoring severely degraded visual media still remains a formidable challenge, as existing methods often hallucinate unnatural textures and contents, struggle with preserving color…
BOOST: Out-of-Distribution-Informed Adaptive Sampling for Bias Mitigation in Stylistic Convolutional Neural Networks
Mridula Vijendran, Shuang Chen, Jingjing Deng +1
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 task…
Artificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Survey
Mridula Vijendran, Jingjing Deng, Shuang Chen +2
Artificial Intelligence significantly enhances the visual art industry by analyzing, identifying and generating digitized artistic images. This review highlights the substantial be…
ST-SACLF: Style Transfer Informed Self-Attention Classifier for Bias-Aware Painting Classification
Mridula Vijendran, Frederick W. B. Li, Jingjing Deng +1
Painting classification plays a vital role in organizing, finding, and suggesting artwork for digital and classic art galleries. Existing methods struggle with adapting knowledge f…
Tackling Data Bias in Painting Classification with Style Transfer
Mridula Vijendran, Frederick W. B. Li, Hubert P. H. Shum
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…