SynthGenNet reports 49.79 mIoU on IDD and 48.33 on Cityscapes by mixing multiple synthetic sources with diverse self-supervised losses, but the method actually uses unlabeled target images during training, so it is not domain generalization.
Semantic segmen- tation of autonomous driving scenes based on multi- scale adaptive attention.Neuroscience-driven Visual Representation, page 97, 2024
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 1years
2025 1verdicts
REJECT 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images
SynthGenNet reports 49.79 mIoU on IDD and 48.33 on Cityscapes by mixing multiple synthetic sources with diverse self-supervised losses, but the method actually uses unlabeled target images during training, so it is not domain generalization.