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 …

In this paper, we aim to devise a universally versatile style transfer method capable of
performing artistic, photo-realistic, and video style transfer jointly, without seeing videos …

The performance of nighttime semantic segmentation is restricted by the poor illumination
and a lack of pixel-wise annotation, which severely limit its application in autonomous …

Remarkable achievements have been attained with Generative Adversarial Networks
(GANs) in image-to-image translation. However, due to a tremendous amount of parameters …

Understanding the spatial heterogeneity of tumours and its links to disease initiation and
progression is a cornerstone of cancer biology. Presently, histopathology workflows heavily …

Style transfer aims to reproduce content images with the styles from reference images.
Existing universal style transfer methods successfully deliver arbitrary styles to original …

To deliver the artistic expression of the target style, recent studies exploit the attention
mechanism owing to its ability to map the local patches of the style image to the …

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

In this paper, we present a Neural Preset technique to address the limitations of existing
color style transfer methods, including visual artifacts, vast memory requirement, and slow …

With the development of computational intelligence algorithms, unsupervised monocular
depth and pose estimation framework, which is driven by warped photometric consistency …