  Building video-language foundation models is costly and difficult due to the
redundant nature of video data and the lack of high-quality video-language
datasets. In this paper, we propose an efficient framework to harvest video
foundation models from image ones. Our method is intuitively simple by randomly
dropping input video patches and masking out input text during the
post-pretraining procedure. The patch dropping boosts the training efficiency
significantly and text masking enforces the learning of cross-modal fusion. We
conduct extensive experiments to validate the effectiveness of our method on a
wide range of video-language downstream tasks including various zero-shot
tasks, video question answering, and video-text retrieval. Despite its
simplicity, our method achieves state-of-the-art performances, which are
comparable to some heavily pretrained video foundation models. Our method is
extremely efficient and can be trained in less than one day on 8 GPUs,
requiring only WebVid-10M as pretraining data. We hope our method can serve as
a simple yet strong counterpart for prevalent video foundation models, provide
useful insights when building them, and make large pretrained models more
accessible and sustainable. This is part of the InternVideo project
\url{https://github.com/OpenGVLab/InternVideo}.
