  Large-scale video-language pre-training has shown significant improvement in
video-language understanding tasks. Previous studies of video-language
pretraining mainly focus on short-form videos (i.e., within 30 seconds) and
sentences, leaving long-form video-language pre-training rarely explored.
Directly learning representation from long-form videos and language may benefit
many long-form video-language understanding tasks. However, it is challenging
due to the difficulty of modeling long-range relationships and the heavy
computational burden caused by more frames. In this paper, we introduce a
Long-Form VIdeo-LAnguage pre-training model (LF-VILA) and train it on a
large-scale long-form video and paragraph dataset constructed from an existing
public dataset. To effectively capture the rich temporal dynamics and to better
align video and language in an efficient end-to-end manner, we introduce two
novel designs in our LF-VILA model. We first propose a Multimodal Temporal
Contrastive (MTC) loss to learn the temporal relation across different
modalities by encouraging fine-grained alignment between long-form videos and
paragraphs. Second, we propose a Hierarchical Temporal Window Attention (HTWA)
mechanism to effectively capture long-range dependency while reducing
computational cost in Transformer. We fine-tune the pre-trained LF-VILA model
on seven downstream long-form video-language understanding tasks of
paragraph-to-video retrieval and long-form video question-answering, and
achieve new state-of-the-art performances. Specifically, our model achieves
16.1% relative improvement on ActivityNet paragraph-to-video retrieval task and
2.4% on How2QA task, respectively. We release our code, dataset, and
pre-trained models at https://github.com/microsoft/XPretrain.

Computer Vision and Pattern Recognition