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arXiv · 2509.04193

DUDE: Diffusion-Based Unsupervised Cross-Domain Image Retrieval

Ruohong Yang·Peng Hu·Yunfan Li·Xi Peng
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Abstract

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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Ruohong Yang, Peng Hu, Yunfan Li, Xi Peng. 2025-09-04. DUDE: Diffusion-Based Unsupervised Cross-Domain Image Retrieval. https://arxiv.org/abs/2509.04193

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