  In this report, we propose a video-language pretraining (VLP) based solution
\cite{kevin2022egovlp} for the EPIC-KITCHENS-100 Multi-Instance Retrieval (MIR)
challenge. 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 to MIR benchmark. Furthermore, we devise
an adaptive multi-instance max-margin loss to effectively fine-tune the model
and equip the dual-softmax technique for reliable inference. Our best single
model obtains strong performance on the challenge test set with 47.39% mAP and
61.44% nDCG. The code is available at https://github.com/showlab/EgoVLP.
