用生成模型分离图像内容与风格，实现无标注跨域图像检索
DUDE: Diffusion-Based Unsupervised Cross-Domain Image Retrieval
- 通过文本生成模型分离物体特征与领域风格
- 在13个领域上三个基准数据集均达领先性能
- 适合需要无监督跨域检索的研究者和开发者
无监督跨域图像检索（UCIR）旨在不依赖标注的情况下，跨不同领域检索同一类别的图像。现有方法对整张图像进行跨域特征对齐，常因物体特征与领域特定风格纠缠而难以应对领域差异。为此，我们提出DUDE，一种基于特征解耦的新型UCIR方法。DUDE利用文本到图像生成模型将物体特征从领域特有风格中分离，从而促进语义图像检索。为进一步实现解耦后物体特征的可靠对齐，DUDE以渐进方式对齐域内与域间互为邻居的样本。大量实验表明，DUDE在三个基准数据集上的13个领域中均达到当前最优性能。代码将公开。
原文摘要 · Abstract (English)
Unsupervised cross-domain image retrieval (UCIR) aims to retrieve images of the same category across diverse domains without relying on annotations. Existing UCIR methods, which align cross-domain features for the entire image, often struggle with the domain gap, as the object features critical for retrieval are frequently entangled with domain-specific styles. To address this challenge, we propose DUDE, a novel UCIR method building upon feature disentanglement. In brief, DUDE leverages a text-to-image generative model to disentangle object features from domain-specific styles, thus facilitating semantical image retrieval. To further achieve reliable alignment of the disentangled object features, DUDE aligns mutual neighbors from within domains to across domains in a progressive manner. Extensive experiments demonstrate that DUDE achieves state-of-the-art performance across three benchmark datasets over 13 domains. The code will be released.
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