Relation-aware Video Reading Comprehension for Temporal Language Grounding

• URL: http://arxiv.org/abs/2110.05717v1
• Date: Tue, 12 Oct 2021 03:10:21 GMT
• Title: Relation-aware Video Reading Comprehension for Temporal Language Grounding
• Authors: Jialin Gao, Xin Sun, Mengmeng Xu, Xi Zhou and Bernard Ghanem
• Abstract summary: Temporal language grounding in videos aims to localize the temporal span relevant to the given query sentence. This paper will formulate temporal language grounding into video reading comprehension and propose a Relation-aware Network (RaNet) to address it.
• Score: 67.5613853693704
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Temporal language grounding in videos aims to localize the temporal span relevant to the given query sentence. Previous methods treat it either as a boundary regression task or a span extraction task. This paper will formulate temporal language grounding into video reading comprehension and propose a Relation-aware Network (RaNet) to address it. This framework aims to select a video moment choice from the predefined answer set with the aid of coarse-and-fine choice-query interaction and choice-choice relation construction. A choice-query interactor is proposed to match the visual and textual information simultaneously in sentence-moment and token-moment levels, leading to a coarse-and-fine cross-modal interaction. Moreover, a novel multi-choice relation constructor is introduced by leveraging graph convolution to capture the dependencies among video moment choices for the best choice selection. Extensive experiments on ActivityNet-Captions, TACoS, and Charades-STA demonstrate the effectiveness of our solution. Codes will be released soon.

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