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

Autor:	Mo, Sicheng, Mu, Fangzhou, Li, Yin
Rok vydání:	2022
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: 5 pages, 2 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.08704 Zobrazit plný text záznamu View this record from Arxiv