  Test-time adaptive (TTA) semantic segmentation adapts a source pre-trained
image semantic segmentation model to unlabeled batches of target domain test
images, different from real-world, where samples arrive one-by-one in an online
fashion. To tackle online settings, we propose TransAdapt, a framework that
uses transformer and input transformations to improve segmentation performance.
Specifically, we pre-train a transformer-based module on a segmentation network
that transforms unsupervised segmentation output to a more reliable supervised
output, without requiring test-time online training. To also facilitate
test-time adaptation, we propose an unsupervised loss based on the transformed
input that enforces the model to be invariant and equivariant to photometric
and geometric perturbations, respectively. Overall, our framework produces
higher quality segmentation masks with up to 17.6% and 2.8% mIOU improvement
over no-adaptation and competitive baselines, respectively.

Computer Vision and Pattern Recognition