  Domain adaptation (DA) approaches address domain shift and enable networks to
be applied to different scenarios. Although various image DA approaches have
been proposed in recent years, there is limited research towards video DA. This
is partly due to the complexity in adapting the different modalities of
features in videos, which includes the correlation features extracted as
long-term dependencies of pixels across spatiotemporal dimensions. The
correlation features are highly associated with action classes and proven their
effectiveness in accurate video feature extraction through the supervised
action recognition task. Yet correlation features of the same action would
differ across domains due to domain shift. Therefore we propose a novel
Adversarial Correlation Adaptation Network (ACAN) to align action videos by
aligning pixel correlations. ACAN aims to minimize the distribution of
correlation information, termed as Pixel Correlation Discrepancy (PCD).
Additionally, video DA research is also limited by the lack of cross-domain
video datasets with larger domain shifts. We, therefore, introduce a novel
HMDB-ARID dataset with a larger domain shift caused by a larger statistical
difference between domains. This dataset is built in an effort to leverage
current datasets for dark video classification. Empirical results demonstrate
the state-of-the-art performance of our proposed ACAN for both existing and the
new video DA datasets.
