  We present Unified Contrastive Arbitrary Style Transfer (UCAST), a novel
style representation learning and transfer framework, which can fit in most
existing arbitrary image style transfer models, e.g., CNN-based, ViT-based, and
flow-based methods. As the key component in image style transfer tasks, a
suitable style representation is essential to achieve satisfactory results.
Existing approaches based on deep neural network typically use second-order
statistics to generate the output. However, these hand-crafted features
computed from a single image cannot leverage style information sufficiently,
which leads to artifacts such as local distortions and style inconsistency. To
address these issues, we propose to learn style representation directly from a
large amount of images based on contrastive learning, by taking the
relationships between specific styles and the holistic style distribution into
account. Specifically, we present an adaptive contrastive learning scheme for
style transfer by introducing an input-dependent temperature. Our framework
consists of three key components, i.e., a parallel contrastive learning scheme
for style representation and style transfer, a domain enhancement module for
effective learning of style distribution, and a generative network for style
transfer. We carry out qualitative and quantitative evaluations to show that
our approach produces superior results than those obtained via state-of-the-art
methods.
