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In this report, we present the technical details of our submission to theEPIC-KITCHENS-100 Unsupervised Domain Adaptation (UDA) Challenge for ActionRecognition 2022. This task aims to adapt an action recognition model trainedon a labeled source domain to an unlabeled target domain. To achieve this goal,we propose an action-aware domain adaptation framework that leverages the priorknowledge induced from the action recognition task during the adaptation.Specifically, we disentangle the source features into action-relevant featuresand action-irrelevant features using the learned action classifier and thenalign the target features with the action-relevant features. To further improvethe action prediction performance, we exploit the verb-noun co-occurrencematrix to constrain and refine the action predictions. Our final submissionachieved the first place in terms of top-1 action recognition accuracy.

Team VI-I2R Technical Report on EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2022