All-to-Key Attention for Arbitrary Style Transfer

Mingrui Zhu, Xiao He, Nannan Wang, Xiaoyu Wang, Xinbo Gao; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 23109-23119

Attention-based arbitrary style transfer studies have shown promising performance in synthesizing vivid local style details. They typically use the all-to-all attention mechanism---each position of content features is fully matched to all positions of style features. However, all-to-all attention tends to generate distorted style patterns and has quadratic complexity, limiting the effectiveness and efficiency of arbitrary style transfer. In this paper, we propose a novel all-to-key attention mechanism---each position of content features is matched to stable key positions of style features---that is more in line with the characteristics of style transfer. Specifically, it integrates two newly proposed attention forms: distributed and progressive attention. Distributed attention assigns attention to key style representations that depict the style distribution of local regions; Progressive attention pays attention from coarse-grained regions to fine-grained key positions. The resultant module, dubbed StyA2K, shows extraordinary performance in preserving the semantic structure and rendering consistent style patterns. Qualitative and quantitative comparisons with state-of-the-art methods demonstrate the superior performance of our approach. Codes and models are available on https://github.com/LearningHx/StyA2K.