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In this report, we propose a video-language pretraining (VLP) based solution\cite{kevin2022egovlp} for four Ego4D challenge tasks, including NaturalLanguage Query (NLQ), Moment Query (MQ), Object State Change Classification(OSCC), and PNR Localization (PNR). Especially, we exploit the recentlyreleased Ego4D dataset \cite{grauman2021ego4d} to pioneer Egocentric VLP frompretraining dataset, pretraining objective, and development set. Based on theabove three designs, we develop a pretrained video-language model that is ableto transfer its egocentric video-text representation or video-onlyrepresentation to several video downstream tasks. Our Egocentric VLP achieves10.46R@1&IoU @0.3 on NLQ, 10.33 mAP on MQ, 74% Acc on OSCC, 0.67 sec error onPNR. The code is available at https://github.com/showlab/EgoVLP.

Egocentric Video-Language Pretraining @ Ego4D Challenge 2022