StyDeco：基于先验蒸馏与语义解耦的无监督风格迁移
StyDeco: Unsupervised Style Transfer with Distilling Priors and Semantic Decoupling
AI总结：
提出StyDeco无监督框架，通过先验引导数据蒸馏和对比语义解耦，解决扩散模型风格迁移中文本引导的语义鸿沟，在三个基准上提升风格保真与结构保留。
摘要：
扩散模型已成为风格迁移的主导范式，但其文本驱动机制受限于一个核心缺陷：它将文本描述视为统一、整体的引导。这一缺陷忽视了文本描述的非空间特性与视觉风格的空间感知属性之间的语义鸿沟，常常导致在风格化过程中丢失语义结构和细粒度细节。本文提出StyDeco，一种无监督框架，通过学习专门针对风格迁移任务的文本表示来解决该缺陷。该框架首先采用先验引导的数据蒸馏（PGD）策略，在无需人工监督的情况下蒸馏风格知识，利用强大的冻结生成模型自动合成伪配对数据。随后，引入对比语义解耦（CSD）目标，通过领域特定权重适配文本编码器，在语义空间中进行两类聚类，促使源表示和目标表示形成不同的簇。在三个经典基准上的大量实验表明，该框架在风格保真度和结构保留方面均优于多种现有方法，突显了其在保持语义的风格迁移中的有效性。此外，该框架支持独特的去风格化过程，进一步展示了其可扩展性。代码见https://github.com/QuanjianSong/StyDeco。
英文摘要：
Diffusion models have emerged as the dominant paradigm for style transfer, but their text-driven mechanism is hindered by a core limitation: it treats textual descriptions as uniform, monolithic guidance. This limitation overlooks the semantic gap between the non-spatial nature of textual descriptions and the spatially-aware attributes of visual style, often leading to the loss of semantic structure and fine-grained details during stylization. In this paper, we propose StyDeco, an unsupervised framework that resolves this limitation by learning text representations specifically tailored for the style transfer task. Our framework first employs Prior-Guided Data Distillation (PGD), a strategy designed to distill stylistic knowledge without human supervision. It leverages a powerful frozen generative model to automatically synthesize pseudo-paired data. Subsequently, we introduce Contrastive Semantic Decoupling (CSD), a task-specific objective that adapts a text encoder using domain-specific weights. CSD performs a two-class clustering in the semantic space, encouraging source and target representations to form distinct clusters. Extensive experiments on three classic benchmarks demonstrate that our framework outperforms several existing approaches in both stylistic fidelity and structural preservation, highlighting its effectiveness in style transfer with semantic preservation. In addition, our framework supports a unique de-stylization process, further demonstrating its extensibility. Our code is vailable at https://github.com/QuanjianSong/StyDeco.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
