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arXiv:2207.01334 (cs)
[Submitted on 4 Jul 2022 (v1), last revised 3 Aug 2022 (this version, v2)]

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

Authors: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 this https URL.
Comments: To appeared in CVPRW22. 5 pages, 2 figures, 2 tables. Code: this https URL. The EPIC challenge technical report of EgoVLP arXiv:2206.01670. See Ego4D challenge technical report arXiv:2207.01622
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2207.01334 [cs.CV]
  (or arXiv:2207.01334v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2207.01334

Submission history

From: Qinghong Lin [view email]
[v1] Mon, 4 Jul 2022 11:32:48 UTC (661 KB)
[v2] Wed, 3 Aug 2022 12:08:50 UTC (660 KB)
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