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Found 2 papers, 1 papers with code
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation
Based on such observation, we propose a depth-aware framework to explicitly leverage depth estimation to mix the categories and facilitate the two complementary tasks, i. e., segmentation and depth learning in an end-to-end manner.
PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation
In an attempt to fill this gap, we propose a unified pixel- and patch-wise self-supervised learning framework, called PiPa, for domain adaptive semantic segmentation that facilitates intra-image pixel-wise correlations and patch-wise semantic consistency against different contexts.
Ranked #1 on
Semantic Segmentation
on SYNTHIA-to-Cityscapes
