Video recognition models have progressed significantly over the past few
years, evolving from shallow classifiers trained on hand-crafted features to
deep spatiotemporal networks. However, labeled video data required to train
such models have not been able to keep up with the ever-increasing depth and
sophistication of these networks. In this work, we propose an alternative
approach to learning video representations that require no semantically labeled
videos and instead leverages the years of effort in collecting and labeling
large and clean still-image datasets. We do so by using state-of-the-art models
pre-trained on image datasets as "teachers" to train video models in a
distillation framework. We demonstrate that our method learns truly
spatiotemporal features, despite being trained only using supervision from
still-image networks. Moreover, it learns good representations across different
input modalities, using completely uncurated raw video data sources and with
different 2D teacher models. Our method obtains strong transfer performance,
outperforming standard techniques for bootstrapping video architectures with
image-based models by 16%. We believe that our approach opens up new approaches
for learning spatiotemporal representations from unlabeled video data.

本文提出了一种另类的方法，通过使用预训练的图像模型作为 “老师” 来训练视频模型，从而实现对视频表示的无监督学习。相比于现有的技术，该方法在 input modalities 的不同数据源下具有更强的迁移性能，并在彻底未被筛选的原始视频数据来源下进行训练。