论文标题
AVID数据集：来自不同国家的匿名视频
AViD Dataset: Anonymized Videos from Diverse Countries
论文作者
论文摘要
我们介绍了一个新的公共视频数据集，以供行动识别：来自不同国家（AVID）的匿名视频。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集合。动机是创建一个公共数据集，该数据集将使每个人都受益于培训和审议行动识别模型，而不是使其对有限国家有用。此外，狂热视频中的所有面部身份都适当地匿名化以保护其隐私。这也是一个静态数据集，每个视频都获得了Creative Commons许可证的许可。我们确认，大多数现有的视频数据集在统计上是偏见，仅捕获了有限数量的国家的动作视频。我们在实验上说明，经过此类偏见数据集训练的模型并不能完美地转移到其他国家的动作视频中，并表明狂热地解决了此类问题。我们还确认，新的AVID数据集可以用作预处理模型的好数据集，比以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.
