  Video analysis tasks such as action recognition have received increasing
research interest with growing applications in fields such as smart healthcare,
thanks to the introduction of large-scale datasets and deep learning-based
representations. However, video models trained on existing datasets suffer from
significant performance degradation when deployed directly to real-world
applications due to domain shifts between the training public video datasets
(source video domains) and real-world videos (target video domains). Further,
with the high cost of video annotation, it is more practical to use unlabeled
videos for training. To tackle performance degradation and address concerns in
high video annotation cost uniformly, the video unsupervised domain adaptation
(VUDA) is introduced to adapt video models from the labeled source domain to
the unlabeled target domain by alleviating video domain shift, improving the
generalizability and portability of video models. This paper surveys recent
progress in VUDA with deep learning. We begin with the motivation of VUDA,
followed by its definition, and recent progress of methods for both closed-set
VUDA and VUDA under different scenarios, and current benchmark datasets for
VUDA research. Eventually, future directions are provided to promote further
VUDA research. The repository of this survey is provided at
https://github.com/xuyu0010/awesome-video-domain-adaptation.
