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AEANet: Affinity Enhanced Attentional Networks for Arbitrary Style Transfer

arXiv 2409.14652 ↗First posted 23 Sep 2024cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 32 references that are in the map. First posted on arXiv 23 Sep 2024.

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References · 27

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  4. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
  5. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 1,998
  6. 2017Universal Style Transfer via Feature Transformscited by 352
  7. 2019Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Designcited by 258
  8. 2018Arbitrary Style Transfer with Style-Attentional Networkscited by 197
  9. 2021StyTr2: Image Style Transfer with Transformerscited by 186
  10. 2016Texture Networks: Feed-forward Synthesis of Textures and Stylized Imagescited by 173
  11. 2019Style Transfer by Relaxed Optimal Transport and Self-Similaritycited by 167
  12. 2018Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decorationcited by 115
15 more
  1. 2021ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flowscited by 113
  2. 2016Fast Patch-based Style Transfer of Arbitrary Stylecited by 92
  3. 2023Agent Attention: On the Integration of Softmax and Linear Attentioncited by 90
  4. 2017StyleBank: An Explicit Representation for Neural Image Style Transfercited by 89
  5. 2020Arbitrary Video Style Transfer via Multi-Channel Correlationcited by 84
  6. 2019Dynamic Instance Normalization for Arbitrary Style Transfercited by 83
  7. 2018Arbitrary Style Transfer with Deep Feature Reshufflecited by 75
  8. 2020Arbitrary Style Transfer via Multi-Adaptation Networkcited by 70
  9. 2017Multi-style Generative Network for Real-time Transfercited by 61
  10. 2017Diversified Texture Synthesis with Feed-forward Networkscited by 59
  11. 2019Attention-aware Multi-stroke Style Transfercited by 47
  12. 2020Manifold Alignment for Semantically Aligned Style Transfercited by 34
  13. 2019Multimodal Style Transfer via Graph Cutscited by 32
  14. 2021In the light of feature distributions: moment matching for Neural Style Transfercited by 26
  15. 2023TSSAT: Two-Stage Statistics-Aware Transformation for Artistic Style Transfercited by 2

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