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Where a Strong Backbone Meets Strong Features -- ActionFormer for Ego4D Moment Queries Challenge

2022-11-16 17:43:26
Fangzhou Mu, Sicheng Mo, Gillian Wang, Yin Li

Abstract

This report describes our submission to the Ego4D Moment Queries Challenge 2022. Our submission builds on ActionFormer, the state-of-the-art backbone for temporal action localization, and a trio of strong video features from SlowFast, Omnivore and EgoVLP. Our solution is ranked 2nd on the public leaderboard with 21.76% average mAP on the test set, which is nearly three times higher than the official baseline. Further, we obtain 42.54% Recall@1x at tIoU=0.5 on the test set, outperforming the top-ranked solution by a significant margin of 1.41 absolute percentage points. Our code is available at this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2211.09074

PDF

https://arxiv.org/pdf/2211.09074.pdf


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