TVQA: Localized, Compositional Video Question Answering
TL
TL;DR
This paper presents TVQA, a large-scale video QA dataset based on 6 popular TV shows, and provides analyses of this new dataset as well as several baselines and a multi-stream end-to-end trainable neural network framework for the TVZA task.
Abstract
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 http://tvqa.cs.unc.edu.
References46 items
1
Expression
2
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
3
MAttNet: Modular Attention Network for Referring Expression Comprehension
4
Object Referring in Videos with Language and Human Gaze
5