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This report describes our submission to the Ego4D Moment Queries Challenge2022. Our submission builds on ActionFormer, the state-of-the-art backbone fortemporal action localization, and a trio of strong video features fromSlowFast, Omnivore and EgoVLP. Our solution is ranked 2nd on the publicleaderboard with 21.76% average mAP on the test set, which is nearly threetimes higher than the official baseline. Further, we obtain 42.54% Recall@1x attIoU=0.5 on the test set, outperforming the top-ranked solution by asignificant margin of 1.41 absolute percentage points. Our code is available athttps://github.com/happyharrycn/actionformer_release.

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