StyleDecoupler: Generalizable Artistic Style Disentanglement
Zexi Jia, Jinchao Zhang, Jie Zhou

TL;DR
StyleDecoupler is a novel framework that disentangles artistic style from content using uni-modal and multi-modal vision models, enabling improved style retrieval and analysis without model fine-tuning.
Contribution
It introduces a universal, plug-and-play style disentanglement method leveraging information theory and uni-modal representations, along with a large-scale artwork dataset for benchmarking.
Findings
Achieves state-of-the-art style retrieval performance
Enables style relationship mapping and generative model evaluation
Operates without fine-tuning on frozen vision-language models
Abstract
Representing artistic style is challenging due to its deep entanglement with semantic content. We propose StyleDecoupler, an information-theoretic framework that leverages a key insight: multi-modal vision models encode both style and content, while uni-modal models suppress style to focus on content-invariant features. By using uni-modal representations as content-only references, we isolate pure style features from multi-modal embeddings through mutual information minimization. StyleDecoupler operates as a plug-and-play module on frozen Vision-Language Models without fine-tuning. We also introduce WeART, a large-scale benchmark of 280K artworks across 152 styles and 1,556 artists. Experiments show state-of-the-art performance on style retrieval across WeART and WikiART, while enabling applications like style relationship mapping and generative model evaluation. We release our method…
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Taxonomy
TopicsAesthetic Perception and Analysis · Generative Adversarial Networks and Image Synthesis · Visual Attention and Saliency Detection
