GroundNLQ @ Ego4D natural language queries challenge 2023

• Main Authors: HOU, Zhijian, JI, Lei, GAO, Difei, ZHONG, Wanjun, YAN, Kun, NGO, Chong-wah, CHAN, Wing-Kwong, NGO, Chong-Wah, DUAN, Nan, SHOU, Mike Zheng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/8416; https://ink.library.smu.edu.sg/context/sis_research/article/9419/viewcontent/2306.15255.pdf
• Institution: Singapore Management University

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-traini...