Style Consistency
Style consistency in AI-generated content, encompassing text, images, and audio, focuses on ensuring generated outputs consistently reflect a desired style while maintaining semantic accuracy. Current research emphasizes disentangling style and content representations within various model architectures, including diffusion models, neural radiance fields, and large language models, often employing techniques like contrastive learning, style-aware encoders, and prompt engineering to achieve this. This research is crucial for improving the quality and reliability of AI-generated content across diverse applications, ranging from creative content generation to medical image analysis and human-computer interaction. The ability to control and maintain style consistency is vital for building trustworthy and user-friendly AI systems.
Papers
RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and Style
Yantao Liu, Zijun Yao, Rui Min, Yixin Cao, Lei Hou, Juanzi Li
Do LLMs write like humans? Variation in grammatical and rhetorical styles
Alex Reinhart, David West Brown, Ben Markey, Michael Laudenbach, Kachatad Pantusen, Ronald Yurko, Gordon Weinberg
TextMastero: Mastering High-Quality Scene Text Editing in Diverse Languages and Styles
Tong Wang, Xiaochao Qu, Ting Liu
FAGStyle: Feature Augmentation on Geodesic Surface for Zero-shot Text-guided Diffusion Image Style Transfer
Yuexing Han, Liheng Ruan, Bing Wang
Data Augmentation Integrating Dialogue Flow and Style to Adapt Spoken Dialogue Systems to Low-Resource User Groups
Zhiyang Qi, Michimasa Inaba