  The advancement of the communication technology and the popularity of the
smart phones foster the booming of video ads. Baidu, as one of the leading
search engine companies in the world, receives billions of search queries per
day. How to pair the video ads with the user search is the core task of Baidu
video advertising. Due to the modality gap, the query-to-video retrieval is
much more challenging than traditional query-to-document retrieval and
image-to-image search. Traditionally, the query-to-video retrieval is tackled
by the query-to-title retrieval, which is not reliable when the quality of
tiles are not high. With the rapid progress achieved in computer vision and
natural language processing in recent years, content-based search methods
becomes promising for the query-to-video retrieval. Benefited from pretraining
on large-scale datasets, some visionBERT methods based on cross-modal attention
have achieved excellent performance in many vision-language tasks not only in
academia but also in industry. Nevertheless, the expensive computation cost of
cross-modal attention makes it impractical for large-scale search in industrial
applications. In this work, we present a tree-based combo-attention network
(TCAN) which has been recently launched in Baidu's dynamic video advertising
platform. It provides a practical solution to deploy the heavy cross-modal
attention for the large-scale query-to-video search. After launching tree-based
combo-attention network, click-through rate gets improved by 2.29\% and
conversion rate get improved by 2.63\%.
