Text-to-image retrieval plays a crucial role across various applications,
including digital libraries, e-commerce platforms, and multimedia databases, by
enabling the search for images using text queries. Despite the advancements in
Multimodal Large Language Models (MLLMs), which offer leading-edge performance,
their applicability in large-scale, varied, and ambiguous retrieval scenarios
is constrained by significant computational demands and the generation of
injective embeddings. This paper introduces the Text2Pic Swift framework,
tailored for efficient and robust retrieval of images corresponding to
extensive textual descriptions in sizable datasets. The framework employs a
two-tier approach: the initial Entity-based Ranking (ER) stage addresses the
ambiguity inherent in lengthy text queries through a
multiple-queries-to-multiple-targets strategy, effectively narrowing down
potential candidates for subsequent analysis. Following this, the Summary-based
Re-ranking (SR) stage further refines these selections based on concise query
summaries. Additionally, we present a novel Decoupling-BEiT-3 encoder,
specifically designed to tackle the challenges of ambiguous queries and to
facilitate both stages of the retrieval process, thereby significantly
improving computational efficiency via vector-based similarity assessments. Our
evaluation, conducted on the AToMiC dataset, demonstrates that Text2Pic Swift
outperforms current MLLMs by achieving up to an 11.06% increase in Recall@1000,
alongside reductions in training and retrieval durations by 68.75% and 99.79%,
respectively.

Text2Pic Swift 框架通过引入两阶段方法（Entity-based Ranking 和 Summary-based Re-ranking）以及 Decoupling-BEiT-3 编码器，在大规模、多样化和具有模糊查询的检索场景下实现了高效和强大的图像检索。