Self-supervised learning is an effective way for label-free model
pre-training, especially in the video domain where labeling is expensive.
Existing self-supervised works in the video domain use varying experimental
setups to demonstrate their effectiveness and comparison across approaches
becomes challenging with no standard benchmark. In this work, we first provide
a benchmark that enables a comparison of existing approaches on the same
ground. Next, we study five different aspects of self-supervised learning
important for videos; 1) dataset size, 2) complexity, 3) data distribution, 4)
data noise, and, 5)feature analysis. To facilitate this study, we focus on
seven different methods along with seven different network architectures and
perform an extensive set of experiments on 5 different datasets with an
evaluation of two different downstream tasks. We present several interesting
insights from this study which span across different properties of pretraining
and target datasets, pretext-tasks, and model architectures among others. We
further put some of these insights to the real test and propose an approach
that requires a limited amount of training data and outperforms existing
state-of-the-art approaches which use 10x pretraining data. We believe this
work will pave the way for researchers to a better understanding of
self-supervised pretext tasks in video representation learning.

本文提出了用于视频领域的自监督学习基准，并对数据集大小、数据分布、数据噪声、自监督预训练架构相关的五个不同方面进行了观察分析，同时提出一种新方法，不需要过多的训练数据，即可超越现有自监督预训练的先进水平。