Video Question Answering is a challenging problem in visual information
retrieval, which provides the answer to the referenced video content according
to the question. However, the existing visual question answering approaches
mainly tackle the problem of static image question, which may be ineffectively
for video question answering due to the insufficiency of modeling the temporal
dynamics of video contents. In this paper, we study the problem of video
question answering by modeling its temporal dynamics with frame-level attention
mechanism. We propose the attribute-augmented attention network learning
framework that enables the joint frame-level attribute detection and unified
video representation learning for video question answering. We then incorporate
the multi-step reasoning process for our proposed attention network to further
improve the performance. We construct a large-scale video question answering
dataset. We conduct the experiments on both multiple-choice and open-ended
video question answering tasks to show the effectiveness of the proposed
method.

本文研究了如何利用带有特征增强的关注网络学习框架，旨在建立一个能够模拟视频内容的时间动态、实现统一视频表示学习的框架，同时在已构建的大型视频问答数据集上验证其性能，结果表明新模型在选择和开放问题任务上具有有效性。