Wei Jing


2021

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Parallel Attention Network with Sequence Matching for Video Grounding
Hao Zhang | Aixin Sun | Wei Jing | Liangli Zhen | Joey Tianyi Zhou | Siow Mong Rick Goh
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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Span-based Localizing Network for Natural Language Video Localization
Hao Zhang | Aixin Sun | Wei Jing | Joey Tianyi Zhou
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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.