Papers › Where a Strong Backbone Meets Strong Features -- ActionFormer for Ego4D Moment Queries...

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

16 Nov 2022arXiv:2211.09074archive 2025-07-28

Fangzhou Mu, Sicheng Mo, Gillian Wang, Yin Li

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 https://github.com/happyharrycn/actionformer_release.

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