HSI: A Holistic Style Injector for Arbitrary Style Transfer
Journal:
arXiv
Published Date:
Feb 5, 2025
Abstract
Attention-based arbitrary style transfer methods have gained significant
attention recently due to their impressive ability to synthesize style details.
However, the point-wise matching within the attention mechanism may overly
focus on local patterns such that neglect the remarkable global features of
style images. Additionally, when processing large images, the quadratic
complexity of the attention mechanism will bring high computational load. To
alleviate above problems, we propose Holistic Style Injector (HSI), a novel
attention-style transformation module to deliver artistic expression of target
style. Specifically, HSI performs stylization only based on global style
representation that is more in line with the characteristics of style transfer,
to avoid generating local disharmonious patterns in stylized images. Moreover,
we propose a dual relation learning mechanism inside the HSI to dynamically
render images by leveraging semantic similarity in content and style, ensuring
the stylized images preserve the original content and improve style fidelity.
Note that the proposed HSI achieves linear computational complexity because it
establishes feature mapping through element-wise multiplication rather than
matrix multiplication. Qualitative and quantitative results demonstrate that
our method outperforms state-of-the-art approaches in both effectiveness and
efficiency.
