EgoPCA: A New Framework for Egocentric Hand-Object Interaction Understanding

作者：Xu Yue, Yong–Lu Li, Zhemin Huang, Michael Xu Liu, Cewu Lu, Yu‐Wing Tai, Chi–Keung Tang · 年份：2023 · DOI：10.1109/iccv51070.2023.00486 · 被引用次数：8 · 研究领域：Human Pose and Action Recognition、Anomaly Detection Techniques and Applications、Stroke Rehabilitation and Recovery

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.