Span-based Localizing Network for Natural Language Video Localization

Hao Zhang, Aixin Sun, Wei Jing, Joey Tianyi Zhou


Abstract
Given an untrimmed video and a text query, natural language video localization (NLVL) is to locate a matching span from the video that semantically corresponds to the query. Existing solutions formulate NLVL either as a ranking task and apply multimodal matching architecture, or as a regression task to directly regress the target video span. In this work, we address NLVL task with a span-based QA approach by treating the input video as text passage. We propose a video span localizing network (VSLNet), on top of the standard span-based QA framework, to address NLVL. The proposed VSLNet tackles the differences between NLVL and span-based QA through a simple and yet effective query-guided highlighting (QGH) strategy. The QGH guides VSLNet to search for matching video span within a highlighted region. Through extensive experiments on three benchmark datasets, we show that the proposed VSLNet outperforms the state-of-the-art methods; and adopting span-based QA framework is a promising direction to solve NLVL.
Anthology ID:
2020.acl-main.585
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6543–6554
Language:
URL:
https://www.aclweb.org/anthology/2020.acl-main.585
DOI:
10.18653/v1/2020.acl-main.585
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.acl-main.585.pdf
Video:
 http://slideslive.com/38929045