基于风格转换注意的循环一致风格转换

为了解决现有艺术风格转换方法难以同时高质量保持图像内容和转换风格模式的问题，引入一种新颖的风格转换注意网络（style-transition attention network,STANet），其包含2个关键部分：一是非对称注意力模块，用于确定参考图像的风格特征；二是循环结构，用于保存图像内容。首先，采用双流架构，分别对风格和内容图像进行编码；其次，将注意力模块无缝集成到编码器中，生成风格注意表征；最后，将模块放入不同的卷积阶段，使编码器变成交错式的，促进从风格流到内容流的分层信息传播。此外，提出了循环一致损失，强制网络以整体方式保留内容结构和风格模式。结果表明：编码器优于传统的双流架构，STANet能用于交换具有任意风格的2幅图像的风格模式，合成更高质量的风格化图像，同时更好地保留了各自的内容。提出的带有风格转换注意的风格转换循环网络，模型风格化图像的内容细节更多，在泛化到任意风格方面获得了良好的效果。

Cycle consistent style transfer based on style-transition attention

In order to solve the problem that the existing art style transfer methods can not maintain high-quality image content and transform style patterns at the same time, a novel style-transition attention network (STANet) was introduced, which consists of two key parts: one is the asymmetric attention module used to determine the style features of the reference image, and the other is the circular structure used to save the content of the image. Firstly, the two-stream architecture was adopted to encode the style and content images.Secondly, the attention module was seamlessly integrated into the encoder to generate the style attention representation. Finally, the module was put into different convolution stages, making the encoder interleaved, and facilitating the flow of hierarchical information from style to content. In addition, a circular consistency loss was proposed to force the network to retain the content structure and style patterns in a holistic manner. The results show that the encoder is superior to the traditional Shuangliu District architecture, and STANet can be used to exchange the style patterns of two images with any style, resulting in higher quality stylized images, while better preserving their own content. The proposed style conversion loop network with attention to style conversion makes the model stylized images more detailed and achieves good performance in generalization to any styles.