  Cross-model retrieval has emerged as one of the most important upgrades for
text-only search engines (SE). Recently, with powerful representation for
pairwise text-image inputs via early interaction, the accuracy of
vision-language (VL) transformers has outperformed existing methods for
text-image retrieval. However, when the same paradigm is used for inference,
the efficiency of the VL transformers is still too low to be applied in a real
cross-modal SE. Inspired by the mechanism of human learning and using
cross-modal knowledge, this paper presents a novel Vision-Language Decomposed
Transformer (VLDeformer), which greatly increases the efficiency of VL
transformers while maintaining their outstanding accuracy. By the proposed
method, the cross-model retrieval is separated into two stages: the VL
transformer learning stage, and the VL decomposition stage. The latter stage
plays the role of single modal indexing, which is to some extent like the term
indexing of a text SE. The model learns cross-modal knowledge from
early-interaction pre-training and is then decomposed into an individual
encoder. The decomposition requires only small target datasets for supervision
and achieves both $1000+$ times acceleration and less than $0.6$\% average
recall drop. VLDeformer also outperforms state-of-the-art visual-semantic
embedding methods on COCO and Flickr30k.
