Content-Adaptive Style Transfer

The development of Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN) has catalysed the emergence of the Neural Style Transfer (NST) field. NST elucidates the computer's comprehension of artistic elements, holding paramount significance in both academic and practical domains. In this paper, we introduce a novel system termed "Content-Adaptive Style Transfer." This system incorporates two additional techniques: object segmentation and image fusion, augmenting the customizability of NST. Our experimental results underscore its capability to execute style transfer exclusively on the foreground, leaving the background unaffected, or to modify the background style while preserving the original foreground. These innovations promise more rational outcomes in specific scenarios.