# Multimodal Style Transfer via Graph Cuts

arXiv: https://arxiv.org/abs/1904.04443 · HTML: https://mlsanity.com/paper/1904.04443/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2019.

## Built on by (most cited first, 30 of 32)

- [Domain-Aware Universal Style Transfer](https://mlsanity.com/paper/2108.04441/) (2021)
- [SCS-Co: Self-Consistent Style Contrastive Learning for Image Harmonization](https://mlsanity.com/paper/2204.13962/) (2022)
- [DynaST: Dynamic Sparse Transformer for Exemplar-Guided Image Generation](https://mlsanity.com/paper/2207.06124/) (2022)
- [Neural Neighbor Style Transfer](https://mlsanity.com/paper/2203.13215/) (2022)
- [AesUST: Towards Aesthetic-Enhanced Universal Style Transfer](https://mlsanity.com/paper/2208.13016/) (2022)
- [Style-ERD: Responsive and Coherent Online Motion Style Transfer](https://mlsanity.com/paper/2203.02574/) (2022)
- [All-to-key Attention for Arbitrary Style Transfer](https://mlsanity.com/paper/2212.04105/) (2022)
- [DivSwapper: Towards Diversified Patch-based Arbitrary Style Transfer](https://mlsanity.com/paper/2101.06381/) (2021)
- [StylerDALLE: Language-Guided Style Transfer Using a Vector-Quantized Tokenizer of a Large-Scale Generative Model](https://mlsanity.com/paper/2303.09268/) (2023)
- [Industrial Style Transfer with Large-scale Geometric Warping and Content Preservation](https://mlsanity.com/paper/2203.12835/) (2022)
- [Quality Evaluation of Arbitrary Style Transfer: Subjective Study and Objective Metric](https://mlsanity.com/paper/2208.00623/) (2022)
- [Few-Shot Model Adaptation for Customized Facial Landmark Detection, Segmentation, Stylization and Shadow Removal](https://mlsanity.com/paper/2104.09457/) (2021)
- [Semi-parametric Makeup Transfer via Semantic-aware Correspondence](https://mlsanity.com/paper/2203.02286/) (2022)
- [MaRINeR: Enhancing Novel Views by Matching Rendered Images with Nearby References](https://mlsanity.com/paper/2407.13745/) (2024)
- [UVStyle-Net: Unsupervised Few-shot Learning of 3D Style Similarity Measure for B-Reps](https://mlsanity.com/paper/2105.02961/) (2021)
- [Spectral Normalized-Cut Graph Partitioning with Fairness Constraints](https://mlsanity.com/paper/2307.12065/) (2023)
- [Painterly Image Harmonization by Learning from Painterly Objects](https://mlsanity.com/paper/2312.10263/) (2023)
- [TSSAT: Two-Stage Statistics-Aware Transformation for Artistic Style Transfer](https://mlsanity.com/paper/2309.06004/) (2023)
- [DSI2I: Dense Style for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2212.13253/) (2022)
- [Progressive Painterly Image Harmonization from Low-level Styles to High-level Styles](https://mlsanity.com/paper/2312.10264/) (2023)
- [HSI: A Holistic Style Injector for Arbitrary Style Transfer](https://mlsanity.com/paper/2502.04369/) (2025)
- [OmniStyle2: Learning to Stylize by Learning to Destylize](https://mlsanity.com/paper/2509.05970/) (2025)
- [Non-Parametric Neural Style Transfer](https://mlsanity.com/paper/2108.12847/) (2021)
- [Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice](https://mlsanity.com/paper/2110.02750/) (2021)
- [Style Transfer with Target Feature Palette and Attention Coloring](https://mlsanity.com/paper/2111.04028/) (2021)
- [Deep Feature Rotation for Multimodal Image Style Transfer](https://mlsanity.com/paper/2202.04426/) (2022)
- [Generating Modern Persian Carpet Map by Style-transfer](https://mlsanity.com/paper/2308.04529/) (2023)
- [AEANet: Affinity Enhanced Attentional Networks for Arbitrary Style Transfer](https://mlsanity.com/paper/2409.14652/) (2024)
- [Coarse-to-Fine Hierarchical Alignment for UAV-based Human Detection using Diffusion Models](https://mlsanity.com/paper/2512.13869/) (2025)
- [Lightweight Unpaired Smartphone ISP Transfer with Semantic Pseudo-Pairing](https://mlsanity.com/paper/2605.07495/) (2026)

## Abstract (arXiv)

An assumption widely used in recent neural style transfer methods is that image styles can be described by global statics of deep features like Gram or covariance matrices. Alternative approaches have represented styles by decomposing them into local pixel or neural patches. Despite the recent progress, most existing methods treat the semantic patterns of style image uniformly, resulting unpleasing results on complex styles. In this paper, we introduce a more flexible and general universal style transfer technique: multimodal style transfer (MST). MST explicitly considers the matching of semantic patterns in content and style images. Specifically, the style image features are clustered into sub-style components, which are matched with local content features under a graph cut formulation. A reconstruction network is trained to transfer each sub-style and render the final stylized result. We also generalize MST to improve some existing methods. Extensive experiments demonstrate the superior effectiveness, robustness, and flexibility of MST.