Title: The Forgettable-Watcher Model for Video Question Answering,
Abstract: A number of visual question answering approaches have been proposed recently,
aiming at understanding the visual scenes by answering the natural language
questions. While the image question answering has drawn significant attention,
video question answering is largely unexplored.
Video-QA is different from Image-QA since the information and the events are
scattered among multiple frames. In order to better utilize the temporal
structure of the videos and the phrasal structures of the answers, we propose
two mechanisms: the re-watching and the re-reading mechanisms and combine them
into the forgettable-watcher model. Then we propose a TGIF-QA dataset for video
question answering with the help of automatic question generation. Finally, we
evaluate the models on our dataset. The experimental results show the
effectiveness of our proposed models.