The asymmetrical retrieval setting is a well suited solution for resource constrained applications such as face recognition and image retrieval. In this setting, a large model is used for indexing the gallery while a lightweight model is used for querying. The key principle in such systems is ensuring that both models share the same embedding space. Most methods in this domain are based on knowledge distillation. While useful, they suffer from several drawbacks: they are upper-bounded by the performance of the single best model found and cannot be extended to use an ensemble of models in a straightforward manner. In this paper we present an approach that does not rely on knowledge distillation, rather it utilizes embedding transformation models. This allows the use of N independently trained and diverse gallery models (e.g., trained on different datasets or having a different architecture) and a single query model. As a result, we improve the overall accuracy beyond that of any single model while maintaining a low computational budget for querying. Additionally, we propose a gallery image rejection method that utilizes the diversity between multiple transformed embeddings to estimate the uncertainty of gallery images.

翻译：非对称检索设置是适用于人脸识别和图像检索等资源受限场景的理想解决方案。在此设置中，大型模型用于索引图库，而轻量级模型用于查询。此类系统的核心原则是确保两个模型共享相同的嵌入空间。该领域的大多数方法基于知识蒸馏。虽然有效，但它们存在若干缺陷：受限于单一最优模型的性能上限，且无法直接扩展为模型集成方案。本文提出了一种不依赖知识蒸馏的方法，而是利用嵌入转换模型。该方法允许使用N个独立训练且多样化的图库模型（例如在不同数据集上训练或具有不同架构的模型）与单一查询模型。由此，我们在保持查询端低计算预算的同时，将整体准确率提升至超越任何单一模型的水平。此外，我们提出了利用多重转换嵌入的多样性来估计图库图像不确定性的图库图像拒绝方法。