Computer Science > Computation and Language
[Submitted on 5 Sep 2018 (v1), last revised 7 May 2019 (this version, v2)]
Title:TVQA: Localized, Compositional Video Question Answering
View PDFAbstract:Recent years have witnessed an increasing interest in image-based question-answering (QA) tasks. However, due to data limitations, there has been much less work on video-based QA. In this paper, we present TVQA, a large-scale video QA dataset based on 6 popular TV shows. TVQA consists of 152,545 QA pairs from 21,793 clips, spanning over 460 hours of video. Questions are designed to be compositional in nature, requiring systems to jointly localize relevant moments within a clip, comprehend subtitle-based dialogue, and recognize relevant visual concepts. We provide analyses of this new dataset as well as several baselines and a multi-stream end-to-end trainable neural network framework for the TVQA task. The dataset is publicly available at this http URL.
Submission history
From: Jie Lei [view email][v1] Wed, 5 Sep 2018 19:14:11 UTC (8,613 KB)
[v2] Tue, 7 May 2019 21:34:05 UTC (8,450 KB)
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