Zero-Shot Domain Generalization via Domain Prompt Learning

The paper introduces Domain Prompt Learning (DoP), a zero‑shot domain generalization method that leverages a large vision‑language model like CLIP to recognize unseen classes in unseen target domains without any source‑domain data. DoP learns domain knowledge through text‑domain learning (TDL), which generates diverse style tokens in the text space, and visual‑domain learning (VDL), which refines these tokens using test visual data via self‑supervised learning. Experiments on DG benchmarks such as PACS, VLCS, OfficeHome, DomainNet, and ImageNet demonstrate that DoP consistently outperforms state‑of‑the‑art DG approaches.

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• llms
• multimodal
• benchmarks