  This paper proposes a self-supervised learning approach for video features
that results in significantly improved performance on downstream tasks (such as
video classification, captioning and segmentation) compared to existing
methods. Our method extends the BERT model for text sequences to the case of
sequences of real-valued feature vectors, by replacing the softmax loss with
noise contrastive estimation (NCE). We also show how to learn representations
from sequences of visual features and sequences of words derived from ASR
(automatic speech recognition), and show that such cross-modal training (when
possible) helps even more.
