SIDA: Synthetic Image Driven Zero-shot Domain Adaptation

Zero-shot domain adaptation is a method for adapting a model to a target domain without utilizing target domain image data.

To enable adaptation without target images, existing studies utilize CLIP's embedding space and text description to simulate target-like style features.

Despite the previous achievements in zero-shot domain adaptation, we observe that these text-driven methods struggle to capture complex real-world variations and significantly increase adaptation time due to their alignment process.

Instead of relying on text descriptions, we explore solutions leveraging image data, which provides diverse and more fine-grained style cues.

In this work, we propose SIDA, a novel and efficient zero-shot domain adaptation method leveraging synthetic images.

To generate synthetic images, we first create detailed, source-like images and apply image translation to reflect the style of the target domain.

We then utilize the style features of these synthetic images as a proxy for the target domain.

Based on these features, we introduce Domain Mix and Patch Style Transfer modules, which enable effective modeling of real-world variations.

In particular, Domain Mix blends multiple styles to expand the intra-domain representations, and Patch Style Transfer assigns different styles to individual patches.

We demonstrate the effectiveness of our method by showing state-of-the-art performance in diverse zero-shot adaptation scenarios, particularly in challenging domains.

Moreover, our approach achieves high efficiency by significantly reducing the overall adaptation time.