Paper ID: 2409.14652

AEANet: Affinity Enhanced Attentional Networks for Arbitrary Style Transfer

Gen Li

Arbitrary artistic style transfer is a field that integrates rational academic research with emotional artistic creation. It aims to produce an image that not only features artistic characteristics of the target style but also preserves the texture structure of the content image itself. Existing style transfer methods primarily rely either on global statistics-based information or local patch-based. As a result, the generated images often either superficially apply a filter to the content image or capture extraneous semantic information from the style image, leading to a significant deviation from the global style. In this paper, we propose Affinity Enhanced-Attentional Networks (AEANet), which include a content affinity-enhanced attention (CAEA) module, style affinity-enhanced attention (SAEA) module, and hybrid attention (HA) module. The CAEA and SAEA modules first use attention to improve content and style representations with a Detail Enhanced(DE) module to reinforce fine details. Then, it aligns the global statistical information of the content and style features to fine-tune the feature information. Subsequently, the HA module adjusts the distribution of style features based on the distribution of content features. Additionally, we introduce affinity attention-based Local Dissimilarity Loss to preserve the affinities between the content and style images. Experimental results demonstrate that our approach outperforms state-of-the-art methods in arbitrary style transfer.

Submitted: Sep 23, 2024