  Driving is challenging in conditions like night, rain, and snow. The lack of
good labeled datasets has hampered progress in scene understanding under such
conditions. Unsupervised domain adaptation (UDA) using large labeled clear-day
datasets is a promising research direction in such cases. Current UDA methods,
however, treat all image pixels uniformly, leading to over-reliance on the
dominant scene backgrounds (e.g., roads, sky, sidewalks) that appear
dramatically different across domains. As a result, they struggle to learn
effective features of smaller and often sparse foreground objects (e.g.,
people, vehicles, signs).
  In this work, we improve UDA training by using in-place image warping to
focus on salient object regions. Our insight is that while backgrounds vary
significantly across domains (e.g., snowy night vs. clear day), object
appearances vary to a lesser extent. Therefore, we design instance-level
saliency guidance to adaptively oversample object regions, which reduces
adverse effects from background context and enhances backbone feature learning.
We then unwarp the better learned features while adapting from source to
target. Our approach improves adaptation across geographies, lighting, and
weather conditions, and is agnostic to the task (segmentation, detection),
domain adaptation algorithm, saliency guidance, and underlying model
architecture. Result highlights include +6.1 mAP50 for BDD100K Clear
$\rightarrow$ DENSE Foggy, +3.7 mAP50 for BDD100K Day $\rightarrow$ Night, +3.0
mAP50 for BDD100K Clear $\rightarrow$ Rainy, and +6.3 mIoU for Cityscapes
$\rightarrow$ ACDC. Our method adds minimal training memory and incurs no
additional inference latency. Please see Appendix for more results and
analysis.
