  Answering questions in the context of videos can be helpful in video
indexing, video retrieval systems, video summarization, learning management
systems and surveillance video analysis. Although there exists a large body of
work on visual question answering, work on video question answering (1) is
limited to domains like movies, TV shows, gameplay, or human activity, and (2)
is mostly based on common sense reasoning. In this paper, we explore a new
frontier in video question answering: answering knowledge-based questions in
the context of news videos. To this end, we curate a new dataset of 12K news
videos spanning across 156 hours with 1M multiple-choice question-answer pairs
covering 8263 unique entities. We make the dataset publicly available. Using
this dataset, we propose a novel approach, NEWSKVQA (Knowledge-Aware News Video
Question Answering) which performs multi-modal inferencing over textual
multiple-choice questions, videos, their transcripts and knowledge base, and
presents a strong baseline.
