  We propose a new two-stage pre-training framework for video-to-text
generation tasks such as video captioning and video question answering: A
generative encoder-decoder model is first jointly pre-trained on massive
image-text data to learn fundamental vision-language concepts, and then adapted
to video data in an intermediate video-text pre-training stage to learn
video-specific skills such as spatio-temporal reasoning. As a result, our
VideoOFA model achieves new state-of-the-art performance on four Video
Captioning benchmarks, beating prior art by an average of 9.7 points in CIDEr
score. It also outperforms existing models on two open-ended Video Question
Answering datasets, showcasing its generalization capability as a universal
video-to-text model.
