English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Recent dominant methods for video-language pre-training (VLP) learntransferable representations from the raw pixels in an end-to-end manner toachieve advanced performance on downstream video-language retrieval. Despitethe impressive results, VLP research becomes extremely expensive with the needfor massive data and a long training time, preventing further explorations. Inthis work, we revitalize region features of sparsely sampled video clips tosignificantly reduce both spatial and temporal visual redundancy towardsdemocratizing VLP research at the same time achieving state-of-the-art results.Specifically, to fully explore the potential of region features, we introduce anovel bidirectional region-word alignment regularization that properlyoptimizes the fine-grained relations between regions and certain words insentences, eliminating the domain/modality disconnections between pre-extractedregion features and text. Extensive results of downstream video-languageretrieval tasks on four datasets demonstrate the superiority of our method onboth effectiveness and efficiency, \textit{e.g.}, our method achieves competingresults with 80\% fewer data and 85\% less pre-training time compared to themost efficient VLP method so far \cite{lei2021less}. The code will be availableat \url{https://github.com/showlab/DemoVLP}.

Revitalize Region Feature for Democratizing Video-Language Pre-training of Retrieval