Unsupervised Collaborative Metric Learning with Mixed-Scale Groups for General Object Retrieval

• URL: http://arxiv.org/abs/2403.10798v1
• Date: Sat, 16 Mar 2024 04:01:50 GMT
• Title: Unsupervised Collaborative Metric Learning with Mixed-Scale Groups for General Object Retrieval
• Authors: Shichao Kan, Yuhai Deng, Yixiong Liang, Lihui Cen, Zhe Qu, Yigang Cen, Zhihai He,
• Abstract summary: This paper presents a novel unsupervised deep metric learning approach, termed unsupervised collaborative metric learning with mixed-scale groups (MS-UGCML) We show that our approach can learn embeddings for objects of varying scales, with an object level and image level mAPs improvement of up to 6.69% and 10.03%, respectively.
• Score: 28.810040080126324
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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