In the field of Image-Text Retrieval (ITR), recent advancements have leveraged large-scale Vision-Language Pretraining (VLP) for Fine-Grained (FG) instance-level retrieval, achieving high accuracy at the cost of increased computational complexity. For Coarse-Grained (CG) category-level retrieval, prominent approaches employ Cross-Modal Hashing (CMH) to prioritise efficiency, albeit at the cost of retrieval performance. Due to differences in methodologies, FG and CG models are rarely compared directly within evaluations in the literature, resulting in a lack of empirical data quantifying the retrieval performance-efficiency tradeoffs between the two. This paper addresses this gap by introducing the \texttt{FiCo-ITR} library, which standardises evaluation methodologies for both FG and CG models, facilitating direct comparisons. We conduct empirical evaluations of representative models from both subfields, analysing precision, recall, and computational complexity across varying data scales. Our findings offer new insights into the performance-efficiency trade-offs between recent representative FG and CG models, highlighting their respective strengths and limitations. These findings provide the foundation necessary to make more informed decisions regarding model selection for specific retrieval tasks and highlight avenues for future research into hybrid systems that leverage the strengths of both FG and CG approaches.

翻译：在图像文本检索领域，近期的进展利用大规模视觉语言预训练进行细粒度实例级检索，以增加计算复杂度为代价实现了高精度。对于粗粒度类别级检索，主流方法采用跨模态哈希以优先保证效率，但这是以牺牲检索性能为代价的。由于方法论的差异，文献中的评估很少直接比较细粒度和粗粒度模型，导致缺乏量化两者间检索性能与效率权衡的经验数据。本文通过引入 \texttt{FiCo-ITR} 库来弥补这一空白，该库标准化了细粒度和粗粒度模型的评估方法，促进了直接比较。我们对两个子领域的代表性模型进行了实证评估，分析了不同数据规模下的精确率、召回率和计算复杂度。我们的研究结果为近期代表性细粒度与粗粒度模型之间的性能-效率权衡提供了新的见解，突显了它们各自的优势与局限。这些发现为针对特定检索任务做出更明智的模型选择决策提供了必要的基础，并指明了未来研究的方向，即开发能够结合细粒度与粗粒度方法优势的混合系统。