Video Question Answering via Knowledge-based Progressive Spatial-Temporal Attention Network

Abstract

Visual Question Answering (VQA) is a challenging task that has gained increasing attention from both the computer vision and the natural language processing communities in recent years. Given a question in natural language, a VQA system is designed to automatically generate the answer according to the referenced visual content. Though there recently has been much intereset in this topic, the existing work of visual question answering mainly focuses on a single static image, which is only a small part of the dynamic and sequential visual data in the real world. As a natural extension, video question answering (VideoQA) is less explored. Because of the inherent temporal structure in the video, the approaches of ImageQA may be ineffectively applied to video question answering. In this article, we not only take the spatial and temporal dimension of video content into account but also employ an external knowledge base to improve the answering ability of the network. More specifically, we propose a knowledge-based progressive spatial-temporal attention network to tackle this problem. We obtain both objects and region features of the video frames from a region proposal network. The knowledge representation is generated by a word-level attention mechanism using the comment information of each object that is extracted from DBpedia. Then, we develop a question-knowledge-guided progressive spatial-temporal attention network to learn the joint video representation for video question answering task. We construct a large-scale video question answering dataset. The extensive experiments based on two different datasets validate the effectiveness of our method.