Although existing artistic style transfer methods have achieved significant improvement with
deep neural networks, they still suffer from artifacts such as disharmonious colors and …

Content and style (CS) disentanglement is a fundamental problem and critical challenge of
style transfer. Existing approaches based on explicit definitions (eg, Gram matrix) or implicit …

State-of-the-art parametric and non-parametric style transfer approaches are prone to either
distorted local style patterns due to global statistics alignment, or unpleasing artifacts …

In this paper, we study the task of synthetic-to-real domain generalized semantic
segmentation, which aims to learn a model that is robust to unseen real-world scenes using …

The mechanism of existing style transfer algorithms is by minimizing a hybrid loss function to
push the generated image toward high similarities in both content and style. However, this …

Recent studies have shown remarkable success in universal style transfer which transfers
arbitrary visual styles to content images. However, existing approaches suffer from the …

Digital art synthesis is receiving increasing attention in the multimedia community because
of engaging the public with art effectively. Current digital art synthesis methods usually use …

Arbitrary style transfer (AST) transfers arbitrary artistic styles onto content images. Despite
the recent rapid progress, existing AST methods are either incapable or too slow to run at …

Abstract Domain shift widely exists in the visual world, while modern deep neural networks
commonly suffer from severe performance degradation under domain shift due to poor …

The field of neural style transfer has experienced a surge of research exploring different
avenues ranging from optimization-based approaches and feed-forward models to meta …