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Recent developments in modeling language and vision have been successfully applied to image question answering. It is both crucial and natural to extend this research direction to the video domain for video question answering (VideoQA). Compared to the image domain where large scale and fully annotated benchmark datasets exists, VideoQA datasets are limited to small scale and are automatically generated, etc. These limitations restrict their applicability in practice. Here we introduce ActivityNet-QA, a fully annotated and large scale VideoQA dataset. The dataset consists of 58,000 QA pairs on 5,800 complex web videos derived from the popular ActivityNet dataset. We present a statistical analysis of our ActivityNet-QA dataset and conduct extensive experiments on it by comparing existing VideoQA baselines. Moreover, we explore various video representation strategies to improve VideoQA performance, especially for long videos. The dataset is available at https://github.com

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Video Question Answering (VideoQA) is the extension of image question answering (ImageQA) in the video domain. Methods are required to give the correct answer after analyzing the provided video and question in this task. Comparing to ImageQA, the most distinctive part is the media type. Both tasks require the understanding of visual media, but VideoQA is much more challenging, mainly because of the complexity and diversity of videos. Particularly, working with the video needs to model its inherent temporal structure and analyze the diverse information it contains. In this article, we propose to tackle the task from a multichannel perspective. Appearance, motion, and audio features are extracted from the video, and question-guided attentions are refined to generate the expressive clues that support the correct answer. We also incorporate the relevant text information acquired from Wikipedia as an attempt to extend the capability of the method. Experiments on TGIF-QA and ActivityNet-QA datasets show the advantages of our method compared to existing methods. We also demonstrate the effectiveness and interpretability of our method by analyzing the refined attention weights during the question-answering procedure.