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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 itsegocentric video-text representation to MIR benchmark. Furthermore, we devisean adaptive multi-instance max-margin loss to effectively fine-tune the modeland equip the dual-softmax technique for reliable inference. Our best singlemodel obtains strong performance on the challenge test set with 47.39% mAP and61.44% nDCG. The code is available at https://github.com/showlab/EgoVLP.

Egocentric Video-Language Pretraining @ EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2022