  Our objective in this work is video-text retrieval - in particular a joint
embedding that enables efficient text-to-video retrieval. The challenges in
this area include the design of the visual architecture and the nature of the
training data, in that the available large scale video-text training datasets,
such as HowTo100M, are noisy and hence competitive performance is achieved only
at scale through large amounts of compute. We address both these challenges in
this paper. We propose an end-to-end trainable model that is designed to take
advantage of both large-scale image and video captioning datasets. Our model is
an adaptation and extension of the recent ViT and Timesformer architectures,
and consists of attention in both space and time. The model is flexible and can
be trained on both image and video text datasets, either independently or in
conjunction. It is trained with a curriculum learning schedule that begins by
treating images as 'frozen' snapshots of video, and then gradually learns to
attend to increasing temporal context when trained on video datasets. We also
provide a new video-text pretraining dataset WebVid-2M, comprised of over two
million videos with weak captions scraped from the internet. Despite training
on datasets that are an order of magnitude smaller, we show that this approach
yields state-of-the-art results on standard downstream video-retrieval
benchmarks including MSR-VTT, MSVD, DiDeMo and LSMDC.
