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We propose a novel video understanding task by fusing knowledge-based andvideo question answering. First, we introduce KnowIT VQA, a video dataset with24,282 human-generated question-answer pairs about a popular sitcom. Thedataset combines visual, textual and temporal coherence reasoning together withknowledge-based questions, which need of the experience obtained from theviewing of the series to be answered. Second, we propose a video understandingmodel by combining the visual and textual video content with specific knowledgeabout the show. Our main findings are: (i) the incorporation of knowledgeproduces outstanding improvements for VQA in video, and (ii) the performance onKnowIT VQA still lags well behind human accuracy, indicating its usefulness forstudying current video modelling limitations.

KnowIT VQA: Answering Knowledge-Based Questions about Videos