  Unsupervised Domain Adaptation (UDA) can transfer knowledge from labeled
source data to unlabeled target data of the same categories. However, UDA for
first-person action recognition is an under-explored problem, with lack of
datasets and limited consideration of first-person video characteristics. This
paper focuses on addressing this problem. Firstly, we propose two small-scale
first-person video domain adaptation datasets: ADL$_{small}$ and GTEA-KITCHEN.
Secondly, we introduce channel-temporal attention blocks to capture the
channel-wise and temporal-wise relationships and model their inter-dependencies
important to first-person vision. Finally, we propose a Channel-Temporal
Attention Network (CTAN) to integrate these blocks into existing architectures.
CTAN outperforms baselines on the two proposed datasets and one existing
dataset EPIC$_{cvpr20}$.
