  This report describes the technical details of our submission to the
EPIC-Kitchens 2021 Unsupervised Domain Adaptation Challenge for Action
Recognition. The EPIC-Kitchens dataset is more difficult than other video
domain adaptation datasets due to multi-tasks with more modalities. Firstly, to
participate in the challenge, we employ a transformer to capture the spatial
information from each modality. Secondly, we employ a temporal attention module
to model temporal-wise inter-dependency. Thirdly, we employ the adversarial
domain adaptation network to learn the general features between labeled source
and unlabeled target domain. Finally, we incorporate multiple modalities to
improve the performance by a three-stream network with late fusion. Our network
achieves the comparable performance with the state-of-the-art baseline T$A^3$N
and outperforms the baseline on top-1 accuracy for verb class and top-5
accuracies for all three tasks which are verb, noun and action. Under the team
name xy9, our submission achieved 5th place in terms of top-1 accuracy for verb
class and all top-5 accuracies.
