  E-commerce search engines comprise a retrieval phase and a ranking phase,
where the first one returns a candidate product set given user queries.
Recently, vision-language pre-training, combining textual information with
visual clues, has been popular in the application of retrieval tasks. In this
paper, we propose a novel V+L pre-training method to solve the retrieval
problem in Taobao Search. We design a visual pre-training task based on
contrastive learning, outperforming common regression-based visual pre-training
tasks. In addition, we adopt two negative sampling schemes, tailored for the
large-scale retrieval task. Besides, we introduce the details of the online
deployment of our proposed method in real-world situations. Extensive
offline/online experiments demonstrate the superior performance of our method
on the retrieval task. Our proposed method is employed as one retrieval channel
of Taobao Search and serves hundreds of millions of users in real time.
