 In this work, we propose to avoid manual annotation and to learn video question answering (VideoQA) from millions of readily-available narrated videos. We propose to automatically generate question-answer pairs from transcribed video narrations leveraging a state-of-the-art text transformer pipeline and obtain a new large-scale VideoQA training dataset .
<meaning-changed>  In this work, we propose to avoid manual annotation and to learn video question answering (VideoQA) from millions of readily-available narrated videos. We propose to automatically generate question-answer pairs from transcribed video narrations leveraging a state-of-the-art text transformer pipeline and obtain a new large-scale VideoQA training dataset .
 In this work, we propose to avoid manual annotation and to learn 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 leveraging a state-of-the-art text transformer pipeline and obtain a new large-scale VideoQA training dataset .