  Self-supervised learning approaches for unsupervised domain adaptation (UDA)
of semantic segmentation models suffer from challenges of predicting and
selecting reasonable good quality pseudo labels. In this paper, we propose a
novel approach of exploiting scale-invariance property of the semantic
segmentation model for self-supervised domain adaptation. Our algorithm is
based on a reasonable assumption that, in general, regardless of the size of
the object and stuff (given context) the semantic labeling should be unchanged.
We show that this constraint is violated over the images of the target domain,
and hence could be used to transfer labels in-between differently scaled
patches. Specifically, we show that semantic segmentation model produces output
with high entropy when presented with scaled-up patches of target domain, in
comparison to when presented original size images. These scale-invariant
examples are extracted from the most confident images of the target domain.
Dynamic class specific entropy thresholding mechanism is presented to filter
out unreliable pseudo-labels. Furthermore, we also incorporate the focal loss
to tackle the problem of class imbalance in self-supervised learning. Extensive
experiments have been performed, and results indicate that exploiting the
scale-invariant labeling, we outperform existing self-supervised based
state-of-the-art domain adaptation methods. Specifically, we achieve 1.3% and
3.8% of lead for GTA5 to Cityscapes and SYNTHIA to Cityscapes with VGG16-FCN8
baseline network.
