  Current text-image approaches (e.g., CLIP) typically adopt dual-encoder
architecture using pre-trained vision-language representation. However, these
models still pose non-trivial memory requirements and substantial incremental
indexing time, which makes them less practical on mobile devices. In this
paper, we present an effective two-stage framework to compress large
pre-trained dual-encoder for lightweight text-image retrieval. The resulting
model is smaller (39% of the original), faster (1.6x/2.9x for processing
image/text respectively), yet performs on par with or better than the original
full model on Flickr30K and MSCOCO benchmarks. We also open-source an
accompanying realistic mobile image search application.
