We describe the 2020 edition of the DeepMind Kinetics human action dataset,
which replenishes and extends the Kinetics-700 dataset. In this new version,
there are at least 700 video clips from different YouTube videos for each of
the 700 classes. This paper details the changes introduced for this new release
of the dataset and includes a comprehensive set of statistics as well as
baseline results using the I3D network.

描述了 DeepMind Kinetics 人类动作数据集的 2020 版本，扩展并更新了 Kinetics-700 数据集，其中包含来自不同 YouTube 视频的至少 700 个视频剪辑，以及介绍了更新的数据集所做的变化和使用 I3D 网络的基线结果的详细统计。

Kinetics-700-2020 人类动作数据集短注

A Short Note on the Kinetics-700-2020 Human Action Dataset

A dominant paradigm for learning-based approaches in computer vision is
training generic models, such as ResNet for image recognition, or I3D for video
understanding, on large datasets and allowing them to discover the optimal
representation for the problem at hand. While this is an obviously attractive
approach, it is not applicable in all scenarios. We claim that action detection
is one such challenging problem - the models that need to be trained are large,
and labeled data is expensive to obtain. To address this limitation, we propose
to incorporate domain knowledge into the structure of the model, simplifying
optimization. In particular, we augment a standard I3D network with a tracking
module to aggregate long term motion patterns, and use a graph convolutional
network to reason about interactions between actors and objects. Evaluated on
the challenging AVA dataset, the proposed approach improves over the I3D
baseline by 5.5% mAP and over the state-of-the-art by 4.8% mAP.

本篇论文提出了一种增强标准 I3D 网络的算法，通过添加跟踪模块和图卷积网络结构，将领域知识融入模型，并在活动行为检测中获得了更好的结果表现。