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arXiv · 2207.01334

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

Kevin Qinghong Lin·Alex Jinpeng Wang·Rui Yan·Eric Zhongcong Xu·Rongcheng Tu·Yanru Zhu·Wenzhe Zhao·Weijie Kong·Chengfei Cai·Hongfa Wang·Wei Liu·Mike Zheng Shou
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Abstract

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.

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Kevin Qinghong Lin, Alex Jinpeng Wang, Rui Yan, Eric Zhongcong Xu, Rongcheng Tu, Yanru Zhu, Wenzhe Zhao, Weijie Kong, Chengfei Cai, Hongfa Wang, Wei Liu, Mike Zheng Shou. 2022-07-04. Egocentric Video-Language Pretraining @ EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2022. https://arxiv.org/abs/2207.01334

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