9 citations · 17 across the 6 of their papers we have counts for
6 papers
DICT: Data Injection and Contrastive Trajectory Refinement for Conditional Image Generation with Diffusion Models
Chunnan Shang, Xin Zhang, Zhizhong Wang +1
Diffusion models have become a dominant paradigm for conditional image generation, yet existing approaches generally follow two directions: task-specific designs that can improve p…
SCSA: A Plug-and-Play Semantic Continuous-Sparse Attention for Arbitrary Semantic Style Transfer
Chunnan Shang, Zhizhong Wang, Hongwei Wang +1
Attention-based arbitrary style transfer methods, including CNN-based, Transformer-based, and Diffusion-based, have flourished and produced high-quality stylized images. However, t…
MicroAST: Towards Super-Fast Ultra-Resolution Arbitrary Style Transfer
Zhizhong Wang, Lei Zhao, Zhiwen Zuo +4
Arbitrary style transfer (AST) transfers arbitrary artistic styles onto content images. Despite the recent rapid progress, existing AST methods are either incapable or too slow to…
DivSwapper: Towards Diversified Patch-based Arbitrary Style Transfer
Zhizhong Wang, Lei Zhao, Haibo Chen +4
Gram-based and patch-based approaches are two important research lines of style transfer. Recent diversified Gram-based methods have been able to produce multiple and diverse styli…
Diversified Arbitrary Style Transfer via Deep Feature Perturbation
Zhizhong Wang, Lei Zhao, Haibo Chen +5
Image style transfer is an underdetermined problem, where a large number of solutions can satisfy the same constraint (the content and style). Although there have been some efforts…
GLStyleNet: Higher Quality Style Transfer Combining Global and Local Pyramid Features
Zhizhong Wang, Lei Zhao, Wei Xing +1
Recent studies using deep neural networks have shown remarkable success in style transfer especially for artistic and photo-realistic images. However, the approaches using global f…