In this report, we present our champion solution for Ego4D Natural Language Queries (NLQ) Challenge in CVPR 2023. Essentially, to accurately ground in a video, an effective egocentric feature extractor and a powerful grounding model are required. Motivated by this, we leverage a two-stage pre-training strategy to train egocentric feature extractors and the grounding model on video narrations, and further fine-tune the model on annotated data. In addition, we introduce a novel grounding model GroundNLQ, which employs a multi-modal multi-scale grounding module for effective video and text fusion and various temporal intervals, especially for long videos. On the blind test set, GroundNLQ achieves 25.67 and 18.18 for R1@IoU=0.3 and R1@IoU=0.5, respectively, and surpasses all other teams by a noticeable margin. Our code will be released at\url{https://github.com/houzhijian/GroundNLQ}.
@article{arxiv.2306.15255,
title = {GroundNLQ @ Ego4D Natural Language Queries Challenge 2023},
author = {Zhijian Hou and Lei Ji and Difei Gao and Wanjun Zhong and Kun Yan and Chao Li and Wing-Kwong Chan and Chong-Wah Ngo and Nan Duan and Mike Zheng Shou},
journal= {arXiv preprint arXiv:2306.15255},
year = {2023}
}
Comments
5 pages, 2 figures, 4 tables, the champion solution for Ego4D Natural Language Queries Challenge in CVPR 2023