The task of searching for visual objects in a large image dataset is difficult because it requires efficient matching and accurate localization of objects that can vary in size. Although the segment anything model (SAM) offers a potential solution for extracting object spatial context, learning embeddings for local objects remains a challenging problem. This paper presents a novel unsupervised deep metric learning approach, termed unsupervised collaborative metric learning with mixed-scale groups (MS-UGCML), devised to learn embeddings for objects of varying scales. Following this, a benchmark of challenges is assembled by utilizing COCO 2017 and VOC 2007 datasets to facilitate the training and evaluation of general object retrieval models. Finally, we conduct comprehensive ablation studies and discuss the complexities faced within the domain of general object retrieval. Our object retrieval evaluations span a range of datasets, including BelgaLogos, Visual Genome, LVIS, in addition to a challenging evaluation set that we have individually assembled for open-vocabulary evaluation. These comprehensive evaluations effectively highlight the robustness of our unsupervised MS-UGCML approach, with an object level and image level mAPs improvement of up to 6.69% and 10.03%, respectively. The code is publicly available at https://github.com/dengyuhai/MS-UGCML.

通过无监督的深度度量学习方法，本文提出了一种新颖的混合比例分组的无监督协同度量学习方法(MS-UGCML)，用于学习不同尺度对象的嵌入表示。同时，通过利用COCO 2017和VOC 2007数据集，组装了一套挑战集，以促进通用对象检索模型的训练和评估。综合评估表明，我们的无监督MS-UGCML方法在对象级别和图像级别的mAP提升了6.69%和10.03%。

无监督的混合尺度群组协作度量学习对于普通物体检索的应用