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"Recent methods for visual question answering rely on large-scale annotated datasets  Manual annotation of questions and answers for videos  however  is tedious  expensive and prevents scalability  In this work  we propose to avoid manual annotation and generate a large-scale training dataset for video question answering making use of automatic cross-modal supervision  We leverage a question generation transformer trained on text data and use it to generate question-answer pairs from transcribed video narrations  Given narrated videos  we then automatically generate the HowToVQA69M dataset with 69M video-questionanswer triplets  To handle the open vocabulary of diverse answers in this dataset  we propose a training procedure based on a contrastive loss between a video-question multimodal transformer and an answer transformer  We introduce the zero-shot VideoQA task and show excellent results  in particular for rare answers  Furthermore  we demonstrate our method to significantly outperform the state of the art on MSRVTT-QA  MSVD-QA  ActivityNet-QA and How2QA  Finally  for a detailed evaluation we introduce iVQA  a new VideoQA dataset with reduced language biases and high-quality redundant manual annotations "
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