AViD Dataset: Anonymized Videos from Diverse Countries
AViD Dataset: Anonymized Videos from Diverse Countries
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发表时间：
2020-07
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通讯作者：
A. Piergiovanni;M. Ryoo
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作者：
A. Piergiovanni;M. Ryoo
我们引入了一个新的用于动作识别的公共视频数据集：来自不同国家的匿名视频（AViD）。与现有的公共视频数据集不同，AViD是来自许多不同国家的动作视频的集合。其动机是创建一个公共数据集，这将有利于每个人的行动识别模型的训练和预训练，而不是使其对有限的国家有用。此外，AViD视频中的所有面部身份都经过了适当的匿名处理，以保护他们的隐私。它也是一个静态数据集，其中每个视频都获得了知识共享许可。我们确认，大多数现有的视频数据集在统计上存在偏差，只能捕获来自有限数量国家的动作视频。我们通过实验证明，使用这种有偏见的数据集训练的模型不能完美地转移到来自其他国家的动作视频，并表明AViD解决了这一问题。我们还证实，新的AViD数据集可以作为预训练模型的良好数据集，其性能与之前的数据集相当或更好。
We introduce a new public video dataset for action recognition: Anonymized Videos from Diverse countries (AViD). Unlike existing public video datasets, AViD is a collection of action videos from many different countries. The motivation is to create a public dataset that would benefit training and pretraining of action recognition models for everybody, rather than making it useful for limited countries. Further, all the face identities in the AViD videos are properly anonymized to protect their privacy. It also is a static dataset where each video is licensed with the creative commons license. We confirm that most of the existing video datasets are statistically biased to only capture action videos from a limited number of countries. We experimentally illustrate that models trained with such biased datasets do not transfer perfectly to action videos from the other countries, and show that AViD addresses such problem. We also confirm that the new AViD dataset could serve as a good dataset for pretraining the models, performing comparably or better than prior datasets.