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- W4386561743 abstract "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" @default.
- W4386561743 created "2023-09-09" @default.
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- W4386561743 date "2023-09-05" @default.
- W4386561743 modified "2023-09-30" @default.
- W4386561743 title "EgoPCA: A New Framework for Egocentric Hand-Object Interaction Understanding" @default.
- W4386561743 doi "https://doi.org/10.48550/arxiv.2309.02423" @default.
- W4386561743 hasPublicationYear "2023" @default.
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