2 papers
cs.CV2026
Explanatory Interactive Machine Learning for Bias Mitigation in Visual Gender Classification
Nathanya Satriani, Djordje SlijepÄeviÄ, Markus Schedl +1
Explanatory interactive learning (XIL) enables users to guide model training in machine learning (ML) by providing feedback on the model's explanations, thereby helping it to focus…
cs.CV2026
Bringing Diversity from Diffusion Models to Semantic-Guided Face Asset Generation
Yunxuan Cai, Sitao Xiang, Zongjian Li +2
Digital modeling and reconstruction of human faces serve various applications. However, its availability is often hindered by the requirements of data capturing devices, manual lab…