DUDE: Diffusion-Based Unsupervised Cross-Domain Image Retrieval

• URL: http://arxiv.org/abs/2509.04193v1
• Date: Thu, 04 Sep 2025 13:15:16 GMT
• Title: DUDE: Diffusion-Based Unsupervised Cross-Domain Image Retrieval
• Authors: Ruohong Yang, Peng Hu, Yunfan Li, Xi Peng,
• Abstract summary: Unsupervised cross-domain image retrieval (UCIR) aims to retrieve images of the same category across diverse domains without relying on annotations.<n>Existing UCIR methods, which align cross-domain features for the entire image, often struggle with the domain gap.<n>We propose DUDE, a novel UCIR method building upon feature disentanglement.
• Score: 25.89035776794712
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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