Abstract
Pre-trained vision-language models (VLMs) excel in multimodal tasks, commonly encoding images as embedding vectors for storage in databases and retrieval via approximate nearest neighbor search (ANNS). However, these models struggle with compositional queries and out-of-distribution (OOD) image-text pairs. Inspired by human cognition's ability to learn from minimal examples, we address this performance gap through few-shot learning approaches specifically designed for image retrieval. We introduce the Few-Shot Text-to-Image Retrieval (FSIR) task and its accompanying benchmark dataset, FSIR-BD - the first to explicitly target image retrieval by text accompanied by reference examples, focusing on the challenging compositional and OOD queries. The compositional part is divided to urban scenes and nature species, both in specific situations or with distinctive features. FSIR-BD contains 38,353 images and 303 queries, with 82% comprising the test corpus (averaging per query 37 positives, ground truth matches, and significant number of hard negatives) and 18% forming the few-shot reference corpus (FSR) of exemplar positive and hard negative images. Additionally, we propose two novel retrieval optimization methods leveraging single shot or few shot reference examples in the FSR to improve performance. Both methods are compatible with any pre-trained image encoder, making them applicable to existing large-scale environments. Our experiments demonstrate that: (1) FSIR-BD provides a challenging benchmark for image retrieval; and (2) our optimization methods outperform existing baselines as measured by mean Average Precision (mAP). Further research into FSIR optimization methods will help narrow the gap between machine and human-level understanding, particularly for compositional reasoning from limited examples.
Abstract (translated)
预训练视觉语言模型（VLMs）在多模态任务中表现出色，通常将图像编码为嵌入向量存储于数据库，并通过近似最近邻搜索（ANNS）进行检索。然而，这些模型在组合查询和分布外（OOD）图像-文本对方面存在不足。受人类通过少量样本学习能力的启发，我们通过专门针对图像检索的少样本学习方法来解决这一性能差距。我们提出了少样本文本到图像检索（FSIR）任务及其配套基准数据集FSIR-BD——这是首个明确针对附带参考示例的文本图像检索的数据集，重点关注具有挑战性的组合查询和分布外查询。组合部分细分为城市场景和自然物种，均涉及特定情境或显著特征。FSIR-BD包含38,353张图像和303个查询，其中82%构成测试语料库（每个查询平均包含37个正样本——即真实匹配，以及大量困难负样本），18%构成少样本参考语料库（FSR），包含示例正样本和困难负样本图像。此外，我们提出了两种新颖的检索优化方法，利用FSR中的单样本或少样本参考示例提升性能。这两种方法兼容任意预训练图像编码器，可应用于现有大规模环境。实验表明：（1）FSIR-BD为图像检索提供了具有挑战性的基准；（2）我们的优化方法在平均精度均值（mAP）指标上优于现有基线。对FSIR优化方法的进一步研究将有助于缩小机器与人类理解之间的差距，尤其在有限样本下的组合推理方面。
URL
https://arxiv.org/abs/2603.25891
