  This report describes Badgers@UW-Madison, our submission to the Ego4D Natural
Language Queries (NLQ) Challenge. Our solution inherits the point-based event
representation from our prior work on temporal action localization, and
develops a Transformer-based model for video grounding. Further, our solution
integrates several strong video features including SlowFast, Omnivore and
EgoVLP. Without bells and whistles, our submission based on a single model
achieves 12.64% Mean R@1 and is ranked 2nd on the public leaderboard.
Meanwhile, our method garners 28.45% (18.03%) R@5 at tIoU=0.3 (0.5), surpassing
the top-ranked solution by up to 5.5 absolute percentage points.
