  We study the domain adaptation task for action recognition, namely domain
adaptive action recognition, which aims to effectively transfer action
recognition power from a label-sufficient source domain to a label-free target
domain. Since actions are performed by humans, it is crucial to exploit human
cues in videos when recognizing actions across domains. However, existing
methods are prone to losing human cues but prefer to exploit the correlation
between non-human contexts and associated actions for recognition, and the
contexts of interest agnostic to actions would reduce recognition performance
in the target domain. To overcome this problem, we focus on uncovering
human-centric action cues for domain adaptive action recognition, and our
conception is to investigate two aspects of human-centric action cues, namely
human cues and human-context interaction cues. Accordingly, our proposed
Human-Centric Transformer (HCTransformer) develops a decoupled human-centric
learning paradigm to explicitly concentrate on human-centric action cues in
domain-variant video feature learning. Our HCTransformer first conducts
human-aware temporal modeling by a human encoder, aiming to avoid a loss of
human cues during domain-invariant video feature learning. Then, by a
Transformer-like architecture, HCTransformer exploits domain-invariant and
action-correlated contexts by a context encoder, and further models
domain-invariant interaction between humans and action-correlated contexts. We
conduct extensive experiments on three benchmarks, namely UCF-HMDB,
Kinetics-NecDrone and EPIC-Kitchens-UDA, and the state-of-the-art performance
demonstrates the effectiveness of our proposed HCTransformer.
