LifeQA: A Real-life Dataset for Video Question Answering
Santiago Castro, Mahmoud Azab, Jonathan Stroud, Cristina Noujaim, Ruoyao Wang, Jia Deng, Rada Mihalcea
Abstract
We introduce LifeQA, a benchmark dataset for video question answering that focuses on day-to-day real-life situations. Current video question answering datasets consist of movies and TV shows. However, it is well-known that these visual domains are not representative of 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 we apply several state-of-the-art video question answering models to provide benchmarks for future research. The full dataset is publicly available at https://lit.eecs.umich.edu/lifeqa/.- Anthology ID:
- 2020.lrec-1.536
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 4352–4358
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.536
- DOI:
- Cite (ACL):
- Santiago Castro, Mahmoud Azab, Jonathan Stroud, Cristina Noujaim, Ruoyao Wang, Jia Deng, and Rada Mihalcea. 2020. LifeQA: A Real-life Dataset for Video Question Answering. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 4352–4358, Marseille, France. European Language Resources Association.
- Cite (Informal):
- LifeQA: A Real-life Dataset for Video Question Answering (Castro et al., LREC 2020)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2020.lrec-1.536.pdf
- Code
- mmazab/LifeQA