Core Concepts
Zero-shot domain adaptation method ZoDi utilizes diffusion models for image transfer and model adaptation, showing improved segmentation performance.
Abstract
This paper introduces ZoDi, a zero-shot domain adaptation method using diffusion-based image transfer. It addresses the challenge of domain shift in segmentation tasks by synthesizing target-like images and training models without target images. The method outperforms existing approaches and offers flexibility in model selection.
1. Abstract
Deep learning models excel in segmentation tasks but struggle with domain shift.
ZoDi proposes zero-shot domain adaptation using diffusion models for image transfer and model training.
Benefits include improved segmentation performance without target images.
2. Introduction
Recognition models perform well within consistent data distributions but suffer from out-of-distribution data.
Domain adaptation techniques aim to address this issue by leveraging unsupervised methods.
Zero-shot domain adaptation is crucial when real target images are unavailable.
3. Methodology: ZoDi Approach
ZoDi comprises zero-shot image transfer and model adaptation stages.
Utilizes diffusion models for transferring source images to the target domain while maintaining layout and content.
Trains segmentation models using both original and synthesized images to learn robust representations.
4. Experiments and Results
Evaluation conducted on various settings like day→night, clear→snow, clear→rain, clear→fog, and real→game.
ZoDi shows consistent improvements over existing methods in most scenarios.
Outperforms state-of-the-art baselines like PØDA and DATUM in certain settings.
5. Conclusion
ZoDi presents a promising approach for zero-shot domain adaptation in segmentation tasks.
Offers practical implications for scenarios where obtaining target images is challenging.
Stats
ZoDi shows benefits over existing methods (+2.3 mIoU in day→night, +4.8 mIoU in clear→snow).
It outperforms DAFormer in some settings (+1.8 mIoU in clear→snow, +4.5 mIoU in clear→rain).
Quotes
"ZoDi leverages powerful diffusion models to transfer source images to the target domain."
"Our implementation will be publicly available."