This report describes our submission to the Ego4D Moment Queries Challenge 2022. Our submission builds on ActionFormer [12], the state-of-the-art backbone for temporal action localization, and a trio of strong video features from SlowFast [5], Omnivore [6] and EgoVLP [10].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.