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

The advancement of the communication technology and the popularity of thesmart phones foster the booming of video ads. Baidu, as one of the leadingsearch engine companies in the world, receives billions of search queries perday. How to pair the video ads with the user search is the core task of Baiduvideo advertising. Due to the modality gap, the query-to-video retrieval ismuch more challenging than traditional query-to-document retrieval andimage-to-image search. Traditionally, the query-to-video retrieval is tackledby the query-to-title retrieval, which is not reliable when the quality oftiles are not high. With the rapid progress achieved in computer vision andnatural language processing in recent years, content-based search methodsbecomes promising for the query-to-video retrieval. Benefited from pretrainingon large-scale datasets, some visionBERT methods based on cross-modal attentionhave achieved excellent performance in many vision-language tasks not only inacademia but also in industry. Nevertheless, the expensive computation cost ofcross-modal attention makes it impractical for large-scale search in industrialapplications. In this work, we present a tree-based combo-attention network(TCAN) which has been recently launched in Baidu's dynamic video advertisingplatform. It provides a practical solution to deploy the heavy cross-modalattention for the large-scale query-to-video search. After launching tree-basedcombo-attention network, click-through rate gets improved by 2.29\% andconversion rate get improved by 2.63\%.

Tree-based Text-Vision BERT for Video Search in Baidu Video Advertising