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  • LifeQA dataset features

    work
    Creator:
    Castro, Santiago, Azab, Mahmoud, Stroud, Jonathan C., Noujaim, Cristina, Wang, Ruoyao, Deng, Jia, and Mihalcea, Rada
    Description:
    We introduce LifeQA, a benchmark dataset for video question answering focusing on daily real-life situations. Current video question-answering datasets consist of movies and TV shows. However, it is well-known that these visual domains do not represent our day-to-day lives. Movies and TV shows, for example, benefit from professional camera movements, clean editing, crisp audio recordings, and scripted dialog between professional actors. While these domains provide a large amount of data for training models, their properties make them unsuitable for testing real-life question-answering systems. Our dataset, by contrast, consists of video clips that represent only real-life scenarios. We collect 275 such video clips and over 2.3k multiple-choice questions. In this paper, we analyze the challenging but realistic aspects of LifeQA and apply several state-of-the-art video question-answering models to provide benchmarks for future research. For more information, refer to  https://lit.eecs.umich.edu/lifeqa/.
    Citation to related publication:
    Castro, S., Azab, M., Stroud, J., Noujaim, C., Wang, R., Deng, J., & Mihalcea, R. (2020, May). LifeQA: A real-life dataset for video question answering. In Proceedings of the Twelfth Language Resources and Evaluation Conference (pp. 4352-4358).  https://aclanthology.org/2020.lrec-1.536/
    Discipline:
    Science