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arXiv · 2209.06650

WildQA: In-the-Wild Video Question Answering

Santiago Castro·Naihao Deng·Pingxuan Huang·Mihai Burzo·Rada Mihalcea
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Abstract

Existing video understanding datasets mostly focus on human interactions, with little attention being paid to the "in the wild" settings, where the videos are recorded outdoors. We propose WILDQA, a video understanding dataset of videos recorded in outside settings. In addition to video question answering (Video QA), we also introduce the new task of identifying visual support for a given question and answer (Video Evidence Selection). Through evaluations using a wide range of baseline models, we show that WILDQA poses new challenges to the vision and language research communities. The dataset is available at https://lit.eecs.umich.edu/wildqa/.

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Santiago Castro, Naihao Deng, Pingxuan Huang, Mihai Burzo, Rada Mihalcea. 2022-09-14. WildQA: In-the-Wild Video Question Answering. https://arxiv.org/abs/2209.06650

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