Results 211 to 220 of about 5,049,293 (271)

Font Style Transfer Using Neural Style Transfer and Unsupervised Cross-domain Transfer

2019
In this paper, we study about font generation and conversion. The previous methods dealt with characters as ones made of strokes. On the contrary, we extract features, which are equivalent to the strokes, from font images and texture or pattern images using deep learning, and transform the design pattern of font images.
Atsushi Narusawa, Wataru Shimoda, Keiji Yanai   +2 more
openaire   +1 more source

AesPA-Net: Aesthetic Pattern-Aware Style Transfer Networks

IEEE International Conference on Computer Vision, 2023
To deliver the artistic expression of the target style, recent studies exploit the attention mechanism owing to its ability to map the local patches of the style image to the corresponding patches of the content image.
Kibeom Hong, Seogkyu Jeon, Junsoo Lee, Namhyuk Ahn, Kunhee Kim, Pilhyeon Lee, Daesik Kim, Youngjung Uh, H. Byun   +8 more
semanticscholar   +1 more source

Z*: Zero-shot Style Transfer via Attention Reweighting

Computer Vision and Pattern Recognition
Despite the remarkable progress in image style transfer, formulating style in the context of art is inherently subjective and challenging. In contrast to existing methods, this study shows that vanilla diffusion models can directly extract style ...
Yingying Deng, Xiangyu He, Fan Tang, Weiming Dong   +3 more
semanticscholar   +1 more source

ArtBank: Artistic Style Transfer with Pre-trained Diffusion Model and Implicit Style Prompt Bank

arXiv.org, 2023
Artistic style transfer aims to repaint the content image with the learned artistic style. Existing artistic style transfer methods can be divided into two categories: small model-based approaches and pre-trained large-scale model-based approaches. Small
Zhanjie Zhang, Quanwei Zhang, Guangyuan Li, Wei Xing, Lei Zhao, Jiakai Sun, Zehua Lan, Junsheng Luan, Yiling Huang, Huaizhong Lin   +9 more
semanticscholar   +1 more source

Style-Specific Neurons for Steering LLMs in Text Style Transfer

Conference on Empirical Methods in Natural Language Processing
Text style transfer (TST) aims to modify the style of a text without altering its original meaning. Large language models (LLMs) demonstrate superior performance across multiple tasks, including TST.
Wen Lai, Viktor Hangya, Alexander Fraser
semanticscholar   +1 more source

Efficient Style-Corpus Constrained Learning for Photorealistic Style Transfer

IEEE Transactions on Image Processing, 2021
Photorealistic style transfer is a challenging task, which demands the stylized image remains real. Existing methods are still suffering from unrealistic artifacts and heavy computational cost. In this paper, we propose a novel Style-Corpus Constrained Learning (SCCL) scheme to address these issues.
Yingxu Qiao, Jiabao Cui, Fuxian Huang, Hongmin Liu, Cuizhu Bao, Xi Li   +5 more
openaire   +2 more sources

Are Large Language Models Actually Good at Text Style Transfer?

International Conference on Natural Language Generation
We analyze the performance of large language models (LLMs) on Text Style Transfer (TST), specifically focusing on sentiment transfer and text detoxification across three languages: English, Hindi, and Bengali.
Sourabrata Mukherjee, Atul Kr. Ojha, Ondrej Dusek   +2 more
semanticscholar   +1 more source

FreeStyle: Free Lunch for Text-guided Style Transfer using Diffusion Models

Pattern Recognition
The rapid development of generative diffusion models has significantly advanced the field of style transfer. However, most current style transfer methods based on diffusion models typically involve a slow iterative optimization process, e.g., model fine ...
Feihong He, Gang Li, Mengyuan Zhang, Leilei Yan, Lingyu Si, Fanzhang Li   +5 more
semanticscholar   +1 more source

Towards Highly Realistic Artistic Style Transfer via Stable Diffusion with Step-aware and Layer-aware Prompt

International Joint Conference on Artificial Intelligence
Artistic style transfer aims to transfer the learned artistic style onto an arbitrary content image, generating artistic stylized images. Existing generative adversarial network-based methods fail to generate highly realistic stylized images and always ...
Zhanjie Zhang, Quanwei Zhang, Huaizhong Lin, Wei Xing, Juncheng Mo, Shuaicheng Huang, Jinheng Xie, Guangyuan Li, Junsheng Luan, Lei Zhao, Dalong Zhang, Lixia Chen   +11 more
semanticscholar   +1 more source

TCSinger: Zero-Shot Singing Voice Synthesis with Style Transfer and Multi-Level Style Control

Conference on Empirical Methods in Natural Language Processing
Zero-shot singing voice synthesis (SVS) with style transfer and style control aims to generate high-quality singing voices with unseen timbres and styles (including singing method, emotion, rhythm, technique, and pronunciation) from audio and text ...
Yu Zhang, Ziyue Jiang, Ruiqi Li, Changhao Pan, Jinzheng He, Rongjie Huang, Chuxin Wang, Zhou Zhao   +7 more
semanticscholar   +1 more source