R2VQ (Recipe-to-Video Questions)
Introduced by Pustejovsky et al. in Designing Multimodal Datasets for NLP Challenges
R2VQ is a dataset designed for testing competence-based comprehension of machines over a multimodal recipe collection, which contains text-video aligned recipes.
A total of 51,331 cooking events are annotated, which contain 19,201 explicit ingredients, 16,338 implicit ingredients, 12,316 explicit props, and 11,868 implicit props.
Papers
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