ICIP 2022 Challenge: Pedcmi, Tood Enhanced By Slicing-Aided Fine-Tuning and inference

Universal style transfer has been proven to be effective through CNN models and VGG networks. However, how well to apply the algorithm's style is a separate issue. This problem is especially evident in high-resolution images in which case the division and color at the boundary lines are more complex than low-resolution images. For the WCT model, if the image segment is smaller than the filter size, it will be blurred. High-resolution images have much larger number of small segments and the existing WCT model cannot render them clearly. This paper proposes two methods. It uses the Wasserstein distance-based optimal transport so that the resulting style distribution is the same up to the secondary statistics when a style image is applied to the content image, and proposes collaborative distillation, a method to overcome the encoder and decoder dependency of the WCT module. We propose a module that combines these two methods to apply subtle style transfer even to high-resolution images.