The Kinetics Human Action Video Dataset
W. Kay
João Carreira
Karen Simonyan
Brian Zhang
Chloe Hillier
Sudheendra Vijayanarasimhan
Fabio Viola
Tim Green
T. Back
Apostol Natsev
Mustafa Suleyman
Andrew Zisserman

Abstract
We describe the DeepMind Kinetics human action video dataset. The dataset contains 400 human action classes, with at least 400 video clips for each action. Each clip lasts around 10s and is taken from a different YouTube video. The actions are human focussed and cover a broad range of classes including human-object interactions such as playing instruments, as well as human-human interactions such as shaking hands. We describe the statistics of the dataset, how it was collected, and give some baseline performance figures for neural network architectures trained and tested for human action classification on this dataset. We also carry out a preliminary analysis of whether imbalance in the dataset leads to bias in the classifiers.
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