AViD Dataset: Anonymized Videos from Diverse Countries

TL;DR

The AViD dataset is a diverse, anonymized collection of action videos from multiple countries designed to improve the fairness and generalization of action recognition models across different regions.

Contribution

This paper introduces AViD, a new public, anonymized, and diverse video dataset from multiple countries, addressing biases in existing datasets and enhancing model transferability.

Findings

Models trained on AViD transfer better across countries.

AViD performs comparably or better than prior datasets for pretraining.

Most existing datasets are biased towards limited countries.

Abstract

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.