AViD数据集：来自不同国家的分析视频
AViD Dataset: Anonymized Videos from Diverse Countries
摘要：
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Ancherized Videos（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，使每个人都能受益于动作识别模型的培训和预培训，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适当的匿名处理，以保护其隐私。它也是一个静态数据集，其中每个视频都使用Creative Commons许可证进行许可。我们确认，大多数现有的视频数据集在统计上有偏差，只捕捉来自有限数量的国家的动作视频。我们的实验表明，使用这种有偏见的数据集训练的模型不能完美地转移到其他国家的动作视频，并表明AViD解决了这个问题。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，比以前的数据集表现更好。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
