  Recent dominant methods for video-language pre-training (VLP) learn
transferable representations from the raw pixels in an end-to-end manner to
achieve advanced performance on downstream video-language retrieval. Despite
the impressive results, VLP research becomes extremely expensive with the need
for massive data and a long training time, preventing further explorations. In
this work, we revitalize region features of sparsely sampled video clips to
significantly reduce both spatial and temporal visual redundancy towards
democratizing VLP research at the same time achieving state-of-the-art results.
Specifically, to fully explore the potential of region features, we introduce a
novel bidirectional region-word alignment regularization that properly
optimizes the fine-grained relations between regions and certain words in
sentences, eliminating the domain/modality disconnections between pre-extracted
region features and text. Extensive results of downstream video-language
retrieval tasks on four datasets demonstrate the superiority of our method on
both effectiveness and efficiency, \textit{e.g.}, our method achieves competing
results with 80\% fewer data and 85\% less pre-training time compared to the
most efficient VLP method so far \cite{lei2021less}. The code will be available
at \url{https://github.com/showlab/DemoVLP}.
