  In unsupervised domain adaptation (UDA), where models are trained on source
data (e.g., synthetic) and adapted to target data (e.g., real-world) without
target annotations, addressing the challenge of significant class imbalance
remains an open issue. Despite considerable progress in bridging the domain
gap, existing methods often experience performance degradation when confronted
with highly imbalanced dense prediction visual tasks like semantic and panoptic
segmentation. This discrepancy becomes especially pronounced due to the lack of
equivalent priors between the source and target domains, turning class
imbalanced techniques used for other areas (e.g., image classification)
ineffective in UDA scenarios. This paper proposes a class-imbalance mitigation
strategy that incorporates class-weights into the UDA learning losses, but with
the novelty of estimating these weights dynamically through the loss gradient,
defining a Gradient-based class weighting (GBW) learning. GBW naturally
increases the contribution of classes whose learning is hindered by
large-represented classes, and has the advantage of being able to automatically
and quickly adapt to the iteration training outcomes, avoiding explicitly
curricular learning patterns common in loss-weighing strategies. Extensive
experimentation validates the effectiveness of GBW across architectures
(convolutional and transformer), UDA strategies (adversarial, self-training and
entropy minimization), tasks (semantic and panoptic segmentation), and datasets
(GTA and Synthia). Analysing the source of advantage, GBW consistently
increases the recall of low represented classes.
