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SIDA: Synthetic Image Driven Zero-shot Domain Adaptation

Created by
  • Haebom

Author

Ye-Chan Kim, Seung-Ju Cha, Si-Woo Kim, Taewhan Kim, Dong-Jin Kim

Outline

This paper presents a zero-shot domain adaptation method that adapts a model to a target domain without target domain image data. Existing studies have used CLIP's embedding space and text descriptions to mimic target style features, but they have limitations in capturing complex real-world changes and long adaptation times. In this paper, we propose a synthetic image-based domain adaptation (SIDA) method that utilizes synthetic images that provide diverse and detailed style information instead of text descriptions. SIDA generates synthetic images that reflect the target domain style through image transformation based on source images, and effectively models real-world changes using the domain mix and patch style transfer modules. Domain mix expands the representation within a domain by mixing various styles, and patch style transfer assigns different styles to individual patches. Experimental results show that our method achieves state-of-the-art performance in various zero-shot adaptation scenarios and significantly reduces the adaptation time, demonstrating high efficiency.

Takeaways, Limitations

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Takeaways:
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A novel approach to exploit synthetic images in zero-shot domain adaptation
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Overcoming the limitations of text-based methods and effectively modeling real-world changes
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Improve performance and efficiency with domain mix and patch style transition modules
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Achieve state-of-the-art performance and reduce adaptation time in a variety of zero-shot adaptation scenarios
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Limitations:
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Performance may be affected by the quality of the synthetic image generation.
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Further research is needed on the generalization performance of the proposed method.
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There is a possibility of overfitting to certain domains.
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Consideration needs to be given to the computational cost of the synthetic image generation process.
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