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 this https URL

通过探测、策划和适应（EgoPCA）的方式，该论文重新思考并提出了一种新的基础框架，旨在推动主体与物体间的自我互动识别；论文提供了全面的预训练集、平衡测试集和新的基准，并通过训练微调的策略实现了与 Ego-HOI 基准的最新性能，同时构建了几种新的有效机制，推进进一步的研究，研究数据与发现将为自我互动理解铺就新的道路。