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Recent developments in modeling language and vision have been successfullyapplied to image question answering. It is both crucial and natural to extendthis research direction to the video domain for video question answering(VideoQA). Compared to the image domain where large scale and fully annotatedbenchmark datasets exists, VideoQA datasets are limited to small scale and areautomatically generated, etc. These limitations restrict their applicability inpractice. Here we introduce ActivityNet-QA, a fully annotated and large scaleVideoQA dataset. The dataset consists of 58,000 QA pairs on 5,800 complex webvideos derived from the popular ActivityNet dataset. We present a statisticalanalysis of our ActivityNet-QA dataset and conduct extensive experiments on itby comparing existing VideoQA baselines. Moreover, we explore various videorepresentation strategies to improve VideoQA performance, especially for longvideos. The dataset is available at https://github.com/MILVLG/activitynet-qa

ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering