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This report describes Badgers@UW-Madison, our submission to the Ego4D NaturalLanguage Queries (NLQ) Challenge. Our solution inherits the point-based eventrepresentation from our prior work on temporal action localization, anddevelops a Transformer-based model for video grounding. Further, our solutionintegrates several strong video features including SlowFast, Omnivore andEgoVLP. Without bells and whistles, our submission based on a single modelachieves 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), surpassingthe top-ranked solution by up to 5.5 absolute percentage points.

A Simple Transformer-Based Model for Ego4D Natural Language Queries Challenge