  There has been abundant work in unsupervised domain adaptation for semantic
segmentation (DAS) seeking to adapt a model trained on images from a labeled
source domain to an unlabeled target domain. While the vast majority of prior
work has studied this as a frame-level Image-DAS problem, a few Video-DAS works
have sought to additionally leverage the temporal signal present in adjacent
frames. However, Video-DAS works have historically studied a distinct set of
benchmarks from Image-DAS, with minimal cross-benchmarking. In this work, we
address this gap. Surprisingly, we find that (1) even after carefully
controlling for data and model architecture, state-of-the-art Image-DAS methods
(HRDA and HRDA+MIC) outperform Video-DAS methods on established Video-DAS
benchmarks (+14.5 mIoU on Viper$\rightarrow$CityscapesSeq, +19.0 mIoU on
Synthia$\rightarrow$CityscapesSeq), and (2) naive combinations of Image-DAS and
Video-DAS techniques only lead to marginal improvements across datasets. To
avoid siloed progress between Image-DAS and Video-DAS, we open-source our
codebase with support for a comprehensive set of Video-DAS and Image-DAS
methods on a common benchmark. Code available at
https://github.com/SimarKareer/UnifiedVideoDA
