Annotating videos is cumbersome, expensive and not scalable. Yet, many strong
video models still rely on manually annotated data. With the recent
introduction of the HowTo100M dataset, narrated videos now offer the
possibility of learning video representations without manual supervision. In
this work we propose a new learning approach, MIL-NCE, capable of addressing
misalignments inherent to narrated videos. With this approach we are able to
learn strong video representations from scratch, without the need for any
manual annotation. We evaluate our representations on a wide range of four
downstream tasks over eight datasets: action recognition (HMDB-51, UCF-101,
Kinetics-700), text-to-video retrieval (YouCook2, MSR-VTT), action localization
(YouTube-8M Segments, CrossTask) and action segmentation (COIN). Our method
outperforms all published self-supervised approaches for these tasks as well as
several fully supervised baselines.

本文介绍了一种新的学习方法，MIL-NCE, 用于从讲述视频中学习强大的视频表示，并能够在不需要手动注释的情况下进行。该方法通过对齐不对称的讲述视频，有效地学习了视频表示。作者在 HMDB-51、UCF-101、Kinetics-700 等多个数据集上进行了评估，证明了该方法优于已发表的自监督方法和多个全监督基准线的表现。