Unsupervised Collaborative Metric Learning with Mixed-Scale Groups for
  General Object Retrieval
Shichao Kan Yuhai Deng Yixiong Liang Lihui Cen Zhe Qu Yigang Cen Zhihai He
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.