Real-world data often follows a long-tailed distribution, where a few head classes occupy
most of the data and a large number of tail classes only contain very limited samples. In …

This article introduces a novel approach to unsupervised image-to-image translation, aiming
to overcome the limitations of existing methods in accurately capturing the shape of the …

Recently, contrastive learning has been proven to be powerful in cross-domain feature
learning and has been widely used in image translation tasks. However, these methods …

Arbitrary style transfer aims to render artistic features from a style reference onto an image
while retaining its original content. Previous methods either focus on learning the holistic …

Although arbitrary style transfer has been a hot topic in computer vision, most existing
methods that directly align style and content features frequently result in unnatural effects in …

In recent years, arbitrary style transfer has gained a lot of attention from researchers.
Although existing methods achieve good results, the generated images are usually biased …

Arbitrary style transfer (AST) is a distinctive technique for transferring artistic style into
content images, with the goal of generating stylized images that approximates real artistic …

Arbitrary style transfer (AST) plays a pivotal role in image processing, as it can impart the
stylistic characteristics of a reference image onto a chosen target content image. However …

Text-driven image stylization aims to synthesize content images with learned textual styles.
Recent studies have shown the potential of the diffusion model for producing rich …

Most arbitrary style transfer methods only consider transferring the features of the style and
content images. Although the pixel-wise style transfer is achieved. It is limited to preserving …