  With the explosive growth of multi-modal information on the Internet,
unimodal search cannot satisfy the requirement of Internet applications.
Text-image retrieval research is needed to realize high-quality and efficient
retrieval between different modalities. Existing text-image retrieval research
is mostly based on general vision-language datasets (e.g. MS-COCO, Flickr30K),
in which the query utterance is rigid and unnatural (i.e. verbosity and
formality). To overcome the shortcoming, we construct a new Compact and
Fragmented Query challenge dataset (named Flickr30K-CFQ) to model text-image
retrieval task considering multiple query content and style, including compact
and fine-grained entity-relation corpus. We propose a novel query-enhanced
text-image retrieval method using prompt engineering based on LLM. Experiments
show that our proposed Flickr30-CFQ reveals the insufficiency of existing
vision-language datasets in realistic text-image tasks. Our LLM-based
Query-enhanced method applied on different existing text-image retrieval models
improves query understanding performance both on public dataset and our
challenge set Flickr30-CFQ with over 0.9% and 2.4% respectively. Our project
can be available anonymously in https://sites.google.com/view/Flickr30K-cfq.
