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VLTSeg: Simple Transfer of CLIP-Based Vision-Language Representations for Domain Generalized Semantic Segmentation
We thus propose a new vision-language approach for domain generalized segmentation, which improves the domain generalization SOTA by 7. 6% mIoU when training on the synthetic GTA5 dataset.
Ranked #1 on
Semantic Segmentation
on Cityscapes test
(using extra training data)
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