Learning Domain-Invariant Temporal Dynamics for Few-Shot Action Recognition

arXiv:2402.12706v1 Announce Type: new
Abstract: 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 …

abstract action recognition arxiv challenges cs.cv data distribution domain dynamics few-shot hypothesis identify key knowledge novel recognition samples shift temporal type