
Zero-shot domain adaptation method ZoDi utilizes diffusion models for image transfer and model adaptation, showing improved segmentation performance.


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zero-shot-domain-adaptation-with-diffusion-based-image-transfer


Zero-Shot Domain Adaptation with Diffusion-Based Image Transfer


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Deep learning models can benefit from zero-shot domain adaptation using diffusion-based image transfer, as demonstrated by ZoDi.


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ZoDi: Zero-Shot Domain Adaptation with Diffusion-Based Image Transfer



A novel framework for Unified Language-driven Zero-shot Domain Adaptation (ULDA) that enables a single model to adapt to diverse target domains without explicit domain-ID knowledge, by leveraging hierarchical context alignment, domain-consistent representation learning, and text-driven feature rectification.


unified-language-driven-zero-shot-domain-adaptation-a-practical-approach-for-robust-segmentation-across-diverse-scenarios


Unified Language-driven Zero-shot Domain Adaptation: A Practical Approach for Robust Segmentation across Diverse Scenarios
