Few-shot action recognition aims at quickly adapting a pre-trained model to the novel data
with a distribution shift using only a limited number of samples. Key challenges include how
to identify and leverage the transferable knowledge learned by the pre-trained model. We
therefore propose CDTD, or Causal Domain-Invariant Temporal Dynamics for knowledge
transfer. To identify the temporally invariant and variant representations, we employ the
causal representation learning methods for unsupervised pertaining, and then tune the …

Few-shot action recognition aims at quickly adapting a pre-trained model to the novel data
with a distribution shift using only a limited number of samples. Key challenges include how
to identify and leverage the transferable knowledge learned by the pre-trained model. Our
central hypothesis is that temporal invariance in the dynamic system between latent
variables lends itself to transferability (domain-invariance). We therefore propose DITeD, or
Domain-Invariant Temporal Dynamics for knowledge transfer. To detect the temporal …