yasserben/DATUM
[CVPR-W 2023] Official Implementation of One-shot Unsupervised Domain Adaptation with Personalized Diffusion Models
When you have a segmentation model trained on one type of visual data (like synthetic images) and need it to work well on a new, unlabeled real-world dataset (like street scenes), this project helps. It takes a single example image from your target domain and uses advanced AI to generate many new, photo-realistic training images that perfectly match the style and diverse contexts of that new domain. This allows an AI researcher or computer vision engineer to adapt their existing segmentation models to perform accurately on new, challenging visual data.
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Use this if you need to rapidly adapt an image segmentation model to a new visual domain with very little data, achieving strong performance without extensive manual labeling.
Not ideal if you have abundant labeled data for your target domain or if your task does not involve adapting computer vision models across different visual styles or environments.
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Python
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Last pushed
Jan 09, 2024
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