A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning
A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning
This work presents Unified Contrastive Arbitrary Style Transfer (UCAST), a novel style representation learning and transfer framework, that can fit in most existing arbitrary image style transfer models, such as CNN-based, ViT-based, and flow-based methods. As the key component in image style transfer tasks, a suitable style representation is essential …