# Style Transfer with Target Feature Palette and Attention Coloring

arXiv: https://arxiv.org/abs/2111.04028 · HTML: https://mlsanity.com/paper/2111.04028/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 40 references that are in the map.
- First posted on arXiv 7 Nov 2021.

## Builds on (most cited first, 25 of 25)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Universal Style Transfer via Feature Transforms](https://mlsanity.com/paper/1705.08086/) (2017)
- [A Learned Representation For Artistic Style](https://mlsanity.com/paper/1610.07629/) (2016)
- [Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis](https://mlsanity.com/paper/1701.02096/) (2017)
- [Arbitrary Style Transfer with Style-Attentional Networks](https://mlsanity.com/paper/1812.02342/) (2018)
- [Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks](https://mlsanity.com/paper/1604.04382/) (2016)
- [AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer](https://mlsanity.com/paper/2108.03647/) (2021)
- [Texture Networks: Feed-forward Synthesis of Textures and Stylized Images](https://mlsanity.com/paper/1603.03417/) (2016)
- [Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration](https://mlsanity.com/paper/1805.03857/) (2018)
- [Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis](https://mlsanity.com/paper/1601.04589/) (2016)
- [Fast Patch-based Style Transfer of Arbitrary Style](https://mlsanity.com/paper/1612.04337/) (2016)
- [Arbitrary Style Transfer via Multi-Adaptation Network](https://mlsanity.com/paper/2005.13219/) (2020)
- [Multi-style Generative Network for Real-time Transfer](https://mlsanity.com/paper/1703.06953/) (2017)
- [Collaborative Distillation for Ultra-Resolution Universal Style Transfer](https://mlsanity.com/paper/2003.08436/) (2020)
- [Attention-aware Multi-stroke Style Transfer](https://mlsanity.com/paper/1901.05127/) (2019)
- [Diversified Arbitrary Style Transfer via Deep Feature Perturbation](https://mlsanity.com/paper/1909.08223/) (2019)
- [Domain-Aware Universal Style Transfer](https://mlsanity.com/paper/2108.04441/) (2021)
- [Single Image Depth Prediction with Wavelet Decomposition](https://mlsanity.com/paper/2106.02022/) (2021)
- [Multimodal Style Transfer via Graph Cuts](https://mlsanity.com/paper/1904.04443/) (2019)
- [Art2Real: Unfolding the Reality of Artworks via Semantically-Aware Image-to-Image Translation](https://mlsanity.com/paper/1811.10666/) (2018)
- [EFANet: Exchangeable Feature Alignment Network for Arbitrary Style Transfer](https://mlsanity.com/paper/1811.10352/) (2018)

## Abstract (arXiv)

Style transfer has attracted a lot of attentions, as it can change a given image into one with splendid artistic styles while preserving the image structure. However, conventional approaches easily lose image details and tend to produce unpleasant artifacts during style transfer. In this paper, to solve these problems, a novel artistic stylization method with target feature palettes is proposed, which can transfer key features accurately. Specifically, our method contains two modules, namely feature palette composition (FPC) and attention coloring (AC) modules. The FPC module captures representative features based on K-means clustering and produces a feature target palette. The following AC module calculates attention maps between content and style images, and transfers colors and patterns based on the attention map and the target palette. These modules enable the proposed stylization to focus on key features and generate plausibly transferred images. Thus, the contributions of the proposed method are to propose a novel deep learning-based style transfer method and present target feature palette and attention coloring modules, and provide in-depth analysis and insight on the proposed method via exhaustive ablation study. Qualitative and quantitative results show that our stylized images exhibit state-of-the-art performance, with strength in preserving core structures and details of the content image.