  In this report, we propose a video-language pretraining (VLP) based solution
\cite{kevin2022egovlp} for four Ego4D challenge tasks, including Natural
Language Query (NLQ), Moment Query (MQ), Object State Change Classification
(OSCC), and PNR Localization (PNR). Especially, we exploit the recently
released Ego4D dataset \cite{grauman2021ego4d} to pioneer Egocentric VLP from
pretraining dataset, pretraining objective, and development set. Based on the
above three designs, we develop a pretrained video-language model that is able
to transfer its egocentric video-text representation or video-only
representation to several video downstream tasks. Our Egocentric VLP achieves
10.46R@1&IoU @0.3 on NLQ, 10.33 mAP on MQ, 74% Acc on OSCC, 0.67 sec error on
PNR. The code is available at https://github.com/showlab/EgoVLP.
