AEANet: Affinity Enhanced Attentional Networks for Arbitrary Style Transfer

TL;DR

This paper introduces AEANet, a novel neural network architecture with affinity-enhanced attention modules and a local dissimilarity loss, significantly improving arbitrary style transfer by better preserving content textures and style features.

Contribution

The paper proposes a new affinity-enhanced attentional network with specialized modules and a novel loss function for improved style transfer quality.

Findings

Achieves superior style transfer results compared to state-of-the-art methods.

Effectively preserves content textures and style characteristics.

Demonstrates robustness across diverse artistic styles.

Abstract

Arbitrary artistic style transfer is a research area that combines rational academic study with emotive artistic creation. It aims to create a new image from a content image according to a target artistic style, maintaining the content's textural structural information while incorporating the artistic characteristics of the style image. However, existing style transfer methods often significantly damage the texture lines of the content image during the style transformation. To address these issues, we propose affinity-enhanced attentional network, which include the content affinity-enhanced attention (CAEA) module, the style affinity-enhanced attention (SAEA) module, and the hybrid attention (HA) module. The CAEA and SAEA modules first use attention to enhance content and style representations, followed by a detail enhanced (DE) module to reinforce detail features. The hybrid attention module adjusts the style feature distribution based on the content feature distribution. We also introduce the local dissimilarity loss based on affinity attention, which better preserves the affinity with content and style images. Experiments demonstrate that our work achieves better results in arbitrary style transfer than other state-of-the-art methods.