AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer

Songhua Liu, Tianwei Lin, Dongliang He, Fu Li, Meiling Wang, Xin Li, Zhengxing Sun, Qian Li, Errui Ding; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 6649-6658

Fast arbitrary neural style transfer has attracted widespread attention from academic, industrial and art communities due to its flexibility in enabling various applications. Existing solutions either attentively fuse deep style feature into deep content feature without considering feature distributions, or adaptively normalize deep content feature according to the style such that their global statistic information is matched. Although effective, leaving shallow feature unexplored or without locally considering feature statistics, they are prone to suffer from unnatural output with unpleasing local distortions. To alleviate this problem, in this paper, we propose a novel Adaptive Attention Normalization (AdaAttN) module to adaptively perform attentive normalization on per-point basis. Specifically, spatial attention score is learnt from both shallow and deep features of content and style images. Then per-point weighted statistics are calculated by regarding a style feature point as a distribution of attention-weighted output of all style feature points. Finally, the content feature is normalized so that they demonstrate the same local feature statistics as the calculated per-point weighted style feature statistics. Besides, a novel local feature loss is derived based on AdaAttN to enhance local visual quality. We also extend AdaAttN to be ready for video style transfer with slight modifications. Extensive experiments demonstrate that our method achieves state-of-the-art arbitrary image/video style transfer. Codes and models will be available.