  In this report, we present the technical details of our submission to the
2022 EPIC-Kitchens Unsupervised Domain Adaptation (UDA) Challenge. Existing UDA
methods align the global features extracted from the whole video clips across
the source and target domains but suffer from the spatial redundancy of feature
matching in video recognition. Motivated by the observation that in most cases
a small image region in each video frame can be informative enough for the
action recognition task, we propose to exploit informative image regions to
perform efficient domain alignment. Specifically, we first use lightweight CNNs
to extract the global information of the input two-stream video frames and
select the informative image patches by a differentiable interpolation-based
selection strategy. Then the global information from videos frames and local
information from image patches are processed by an existing video adaptation
method, i.e., TA3N, in order to perform feature alignment for the source domain
and the target domain. Our method (without model ensemble) ranks 4th among this
year's teams on the test set of EPIC-KITCHENS-100.
