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This paper presents a new video question answering task on screencasttutorials. We introduce a dataset including question, answer and contexttriples from the tutorial videos for a software. Unlike other video questionanswering works, all the answers in our dataset are grounded to the domainknowledge base. An one-shot recognition algorithm is designed to extract thevisual cues, which helps enhance the performance of video question answering.We also propose several baseline neural network architectures based on variousaspects of video contexts from the dataset. The experimental resultsdemonstrate that our proposed models significantly improve the questionanswering performances by incorporating multi-modal contexts and domainknowledge.

Video Question Answering on Screencast Tutorials