Style Extraction

Style extraction focuses on isolating and representing stylistic elements from various data modalities, such as images, text, and even handwriting, to enable style transfer or generation tasks. Current research emphasizes disentangling style from content using techniques like dual-level prompt learning (for text) and specialized neural networks (for images and audio), often incorporating transformers or other deep learning architectures to capture complex relationships. These advancements have implications across diverse fields, improving image stylization, personalized text generation, and even enhancing the robustness of geo-localization systems by mitigating environmental variations.