chen742/DCF
This is the official implementation of "Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation" (Accepted at ACM MM 2024 as Oral).
This project helps computer vision researchers and engineers improve the accuracy of semantic scene segmentation models, especially when high-quality real-world labeled data is scarce. It takes synthetic images (like from games or simulations) and unlabeled real-world images (like street scenes) to produce a more precise scene segmentation model capable of identifying objects like buildings, sidewalks, and sky in real photographs.
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Use this if you need to train a robust scene segmentation model for real-world applications but only have access to synthetic data with labels and a large amount of unlabeled real-world images.
Not ideal if you already have perfectly labeled real-world datasets for your target domain or if your task doesn't involve adapting models trained on synthetic environments.
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Language
Python
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Last pushed
Aug 24, 2024
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