融合交叉注意力机制的图像任意风格迁移

Image Arbitrary Style Transfer via Criss-cross Attention

图像风格迁移指将一张普通照片转化为一张具有其他艺术风格效果的图像,随着深度学习的发展,出现了一些图像任意风格迁移算法,给定任意风格便能生成具有该风格的风格化图像。针对任意风格迁移算法中存在如何同时适应全局和局部风格,保持空间一致性问题,提出了一个融合交叉注意力的任意风格迁移算法网络,通过捕捉长程依赖,高效生成全局与局部风格协调的风格化图像;针对风格化图像的内容结构扭曲问题,在进行风格迁移之前,加入一组并行的通道空间注意力网络,该注意力网络能进一步细化关键特征,保留关键信息;除此之外,提出了一个新的损失函数,在消除伪影的同时能更好地保留内容结构信息。该算法能根据内容图像的语义空间分布,匹配语义上最接近的风格特征,高效灵活地调整局部风格,且能保留更多内容结构的原始信息。实验结果表明,所提算法能够生成任意风格且视觉效果更佳的高质量风格化图像。

Arbitrary style transfer is a technique for transferring an ordinary photo to an image with another artistic style.With the development of deep learning,some image arbitrary style transfer algorithms have emerged to generate stylized images with arbitrary styles.To solve the problems in adapting to both global and local styles,maintaining spatial consistency,this paper proposes an arbitrary style transfer via criss-cross attention network,which can efficiently generate stylized images with coordinated global and local styles by capturing long-range dependencies.To address the problem of the distorted content structure of stylized images,a group of the parallel channel and spatial attention networks are added before style transfer,which can further emphasize key features and retain key information.In addition,a new loss function is proposed to eliminate artifacts while preserving the structural information of the content images.This algorithm can match the closest semantic style feature to the content feature,and adjust the local style efficiently and flexibly according to the semantic spatial distribution of the content image.Moreover,it can retain more original information about the structure.The experimental results show that the proposed method can transfer the image into different styles with higher quality and better visual effects.