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In this report, we present the technical details of our submission to the2022 EPIC-Kitchens Unsupervised Domain Adaptation (UDA) Challenge. Existing UDAmethods align the global features extracted from the whole video clips acrossthe source and target domains but suffer from the spatial redundancy of featurematching in video recognition. Motivated by the observation that in most casesa small image region in each video frame can be informative enough for theaction recognition task, we propose to exploit informative image regions toperform efficient domain alignment. Specifically, we first use lightweight CNNsto extract the global information of the input two-stream video frames andselect the informative image patches by a differentiable interpolation-basedselection strategy. Then the global information from videos frames and localinformation from image patches are processed by an existing video adaptationmethod, i.e., TA3N, in order to perform feature alignment for the source domainand the target domain. Our method (without model ensemble) ranks 4th among thisyear's teams on the test set of EPIC-KITCHENS-100.

EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2022: Team HNU-FPV Technical Report