A large part of the current success of deep learning lies in the
effectiveness of data -- more precisely: labelled data. Yet, labelling a
dataset with human annotation continues to carry high costs, especially for
videos. While in the image domain, recent methods have allowed to generate
meaningful (pseudo-) labels for unlabelled datasets without supervision, this
development is missing for the video domain where learning feature
representations is the current focus. In this work, we a) show that
unsupervised labelling of a video dataset does not come for free from strong
feature encoders and b) propose a novel clustering method that allows
pseudo-labelling of a video dataset without any human annotations, by
leveraging the natural correspondence between the audio and visual modalities.
An extensive analysis shows that the resulting clusters have high semantic
overlap to ground truth human labels. We further introduce the first
benchmarking results on unsupervised labelling of common video datasets
Kinetics, Kinetics-Sound, VGG-Sound and AVE.

本文提出了一种基于音频和视觉的聚类方法，可以实现对视频数据集的无监督标记。经过广泛的分析，结果聚类与人工标签有很高的语义重叠性。同时，该方法也为 Kinetics，Kinetics-Sound，VGG-Sound 和 AVE 等常见视频数据集的无监督标记带来了首批基准结果。