FiCo-ITR：弥合细粒度与粗粒度图像-文本检索以进行性能比较分析
FiCo-ITR: bridging fine-grained and coarse-grained image-text retrieval for comparative performance analysis
AI总结：
FiCo-ITR通过标准化评估方法，对比细粒度与粗粒度图像-文本检索模型的性能与效率权衡，为模型选择和混合系统研究提供依据。
摘要：
在图像-文本检索（ITR）领域，近期进展利用大规模视觉语言预训练（VLP）实现了细粒度（FG）实例级检索，取得了高精度，但以增加计算复杂性为代价。对于粗粒度（CG）类别级检索，主流方法采用跨模态哈希（CMH）以优先考虑效率，但牺牲了检索性能。由于方法学差异，FG和CG模型在文献中很少直接比较，导致缺乏量化两者检索性能-效率权衡的实证数据。本文通过引入FiCo-ITR库，标准化FG和CG模型的评估方法，促进直接比较。我们对两个子领域的代表性模型进行了实证评估，分析了在不同数据规模下的精度、召回率和计算复杂性。我们的发现为近期代表性FG和CG模型的性能-效率权衡提供了新见解，突显了各自的优势和局限性。这些发现为特定检索任务的模型选择提供了必要的基础，并指明了未来研究的方向，即研究结合FG和CG方法优势的混合系统。
英文摘要：
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 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.
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