  Domain adaptation is essential for activity recognition to ensure accurate
and robust performance across diverse environments, sensor types, and data
sources. Unsupervised domain adaptation methods have been extensively studied,
yet, they require large-scale unlabeled data from the target domain. In this
work, we focus on Few-Shot Domain Adaptation for Activity Recognition
(FSDA-AR), which leverages a very small amount of labeled target videos to
achieve effective adaptation. This approach is appealing for applications
because it only needs a few or even one labeled example per class in the target
domain, ideal for recognizing rare but critical activities. However, the
existing FSDA-AR works mostly focus on the domain adaptation on sports videos,
where the domain diversity is limited. We propose a new FSDA-AR benchmark using
five established datasets considering the adaptation on more diverse and
challenging domains. Our results demonstrate that FSDA-AR performs comparably
to unsupervised domain adaptation with significantly fewer labeled target
domain samples. We further propose a novel approach, RelaMiX, to better
leverage the few labeled target domain samples as knowledge guidance. RelaMiX
encompasses a temporal relational attention network with relation dropout,
alongside a cross-domain information alignment mechanism. Furthermore, it
integrates a mechanism for mixing features within a latent space by using the
few-shot target domain samples. The proposed RelaMiX solution achieves
state-of-the-art performance on all datasets within the FSDA-AR benchmark. To
encourage future research of few-shot domain adaptation for activity
recognition, our code will be publicly available at
https://github.com/KPeng9510/RelaMiX.
