Dual Attention Mechanism Based Outline Loss for Image Stylization

Image stylization has attracted considerable attention from various fields. Although impressive results have been achieved, existing methods pay less attention to the preservation of outline and putting constraint on it when training, which makes generated images suffering from different degrees of distortion. To address this issue, we propose a dual attention mechanism based outline loss to enhance the restriction of outline consistency by incorporating an outline detection module and a dual attention module. Specifically, an outline detection module is used to detect outlines of the source image and the stylized image, which are further compared and enforced to be consistent with each other by a carefully-elaborated outline loss. Additionally, the dual attention module first guides the model to focus on regions of the source image whose style has the biggest difference from the target image during stylization based on the style attention feature map obtained by the auxiliary classifier. Then, an outline attention map is predicted to highlight regions where the outlines are prone to distort during stylization, which further facilitates the outline loss to execute stronger constraint on these regions. Experimental results show the superiority of our method compared to the existing state-of-the-art methods