StyMam: A Mamba-Based Generator for Artistic Style Transfer
Zhou Hong, Ning Dong, Yicheng Di, Xiaolong Xu, Rongsheng Hu, Yihua Shao, Run Ling, Yun Wang, Juqin Wang, Zhanjie Zhang, Ao Ma

TL;DR
StyMam introduces a novel mamba-based generator with dual-path and attention mechanisms for high-quality, artifact-free artistic style transfer, outperforming existing GAN and diffusion methods in quality and speed.
Contribution
The paper proposes a mamba-based generator with residual dual-path scanning and spatial attention for improved style transfer, addressing limitations of GAN and diffusion approaches.
Findings
Outperforms state-of-the-art methods in quality and speed
Produces artifact-free stylized images
Effectively captures local textures and global dependencies
Abstract
Image style transfer aims to integrate the visual patterns of a specific artistic style into a content image while preserving its content structure. Existing methods mainly rely on the generative adversarial network (GAN) or stable diffusion (SD). GAN-based approaches using CNNs or Transformers struggle to jointly capture local and global dependencies, leading to artifacts and disharmonious patterns. SD-based methods reduce such issues but often fail to preserve content structures and suffer from slow inference. To address these issues, we revisit GAN and propose a mamba-based generator, termed as StyMam, to produce high-quality stylized images without introducing artifacts and disharmonious patterns. Specifically, we introduce a mamba-based generator with a residual dual-path strip scanning mechanism and a channel-reweighted spatial attention module. The former efficiently captures…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Image Enhancement Techniques · Aesthetic Perception and Analysis
