  With the surge in attention to Egocentric Hand-Object Interaction (Ego-HOI),
large-scale datasets such as Ego4D and EPIC-KITCHENS have been proposed.
However, most current research is built on resources derived from third-person
video action recognition. This inherent domain gap between first- and
third-person action videos, which have not been adequately addressed before,
makes current Ego-HOI suboptimal. This paper rethinks and proposes a new
framework as an infrastructure to advance Ego-HOI recognition by Probing,
Curation and Adaption (EgoPCA). We contribute comprehensive pre-train sets,
balanced test sets and a new baseline, which are complete with a
training-finetuning strategy. With our new framework, we not only achieve
state-of-the-art performance on Ego-HOI benchmarks but also build several new
and effective mechanisms and settings to advance further research. We believe
our data and the findings will pave a new way for Ego-HOI understanding. Code
and data are available at https://mvig-rhos.com/ego_pca
