Just Ask: Learning to Answer Questions from Millions of Narrated Videos

• URL: http://arxiv.org/abs/2012.00451v2
• Date: Tue, 30 Mar 2021 14:33:37 GMT
• Title: Just Ask: Learning to Answer Questions from Millions of Narrated Videos
• Authors: Antoine Yang, Antoine Miech, Josef Sivic, Ivan Laptev, Cordelia Schmid
• Abstract summary: We propose to avoid manual annotation and generate a large-scale training dataset for video question answering. We leverage a question generation transformer trained on text data and use it to generate question-answer pairs from transcribed video narrations. We show our method to significantly outperform the state of the art on MSRVTT-QA, MSVD-QA, ActivityNet-QA and How2QA.
• Score: 97.44376735445454
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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-question-answer 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 multi-modal 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 a new VideoQA dataset with reduced language biases and high-quality redundant manual annotations. Our code and datasets will be made publicly available at https://antoyang.github.io/just-ask.html.

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