Flickr30K-CFQ: A Compact and Fragmented Query Dataset for Text-image Retrieval
<|reference_start|>Flickr30K-CFQ: A Compact and Fragmented Query Dataset for Text-image Retrieval: 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.<|reference_end|>
@article{liu2024flickr30k-cfq:,
    title={Flickr30K-CFQ: A Compact and Fragmented Query Dataset for Text-image
  Retrieval},
    author={Haoyu Liu, Yaoxian Song, Xuwu Wang, Zhu Xiangru, Zhixu Li, Wei Song,
  Tiefeng Li},
    journal={arXiv preprint arXiv:2403.13317},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.13317},
    primaryClass={cs.IR}
}