  In this work, we tackle the problem of unsupervised domain adaptation (UDA)
for video action recognition. Our approach, which we call UNITE, uses an image
teacher model to adapt a video student model to the target domain. UNITE first
employs self-supervised pre-training to promote discriminative feature learning
on target domain videos using a teacher-guided masked distillation objective.
We then perform self-training on masked target data, using the video student
model and image teacher model together to generate improved pseudolabels for
unlabeled target videos. Our self-training process successfully leverages the
strengths of both models to achieve strong transfer performance across domains.
We evaluate our approach on multiple video domain adaptation benchmarks and
observe significant improvements upon previously reported results.
