A large pool of diverse artistic styles for style-transfer augmentation improves domain generalization in driving vision models more than repeated use of few styles or domain-matched styles, yielding the lightweight StyleMixDG method with gains on GTAV-to-real benchmarks.
In: CVPR (2022)
2 Pith papers cite this work. Polarity classification is still indexing.
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Dual use of SAM for broader target pixel learning and DINOv3 for domain-invariant prototypes yields +1.3% and +1.4% mIoU gains over baselines on GTA-to-Cityscapes and SYNTHIA-to-Cityscapes.
citing papers explorer
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Evaluation of Randomization through Style Transfer for Enhanced Domain Generalization
A large pool of diverse artistic styles for style-transfer augmentation improves domain generalization in driving vision models more than repeated use of few styles or domain-matched styles, yielding the lightweight StyleMixDG method with gains on GTAV-to-real benchmarks.
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Dual-Foundation Models for Unsupervised Domain Adaptation
Dual use of SAM for broader target pixel learning and DINOv3 for domain-invariant prototypes yields +1.3% and +1.4% mIoU gains over baselines on GTA-to-Cityscapes and SYNTHIA-to-Cityscapes.