Instance Image Retrieval by Learning Purely From Within the Dataset

• URL: http://arxiv.org/abs/2208.06119v1
• Date: Fri, 12 Aug 2022 04:51:53 GMT
• Title: Instance Image Retrieval by Learning Purely From Within the Dataset
• Authors: Zhongyan Zhang, Lei Wang, Yang Wang, Luping Zhou, Jianjia Zhang, Peng Wang, and Fang Chen
• Abstract summary: This work looks into a different approach which has not been well investigated for instance image retrieval previously. By adding an object proposal generator to generate image regions for self-supervised learning, the investigated approach can successfully learn feature representation specific to a given dataset for retrieval. As experimentally validated, such a simple self-supervised learning + self-boosting'' approach can well compete with the relevant state-of-the-art retrieval methods.
• Score: 26.59077238274763
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quality feature representation is key to instance image retrieval. To attain it, existing methods usually resort to a deep model pre-trained on benchmark datasets or even fine-tune the model with a task-dependent labelled auxiliary dataset. Although achieving promising results, this approach is restricted by two issues: 1) the domain gap between benchmark datasets and the dataset of a given retrieval task; 2) the required auxiliary dataset cannot be readily obtained. In light of this situation, this work looks into a different approach which has not been well investigated for instance image retrieval previously: {can we learn feature representation \textit{specific to} a given retrieval task in order to achieve excellent retrieval?} Our finding is encouraging. By adding an object proposal generator to generate image regions for self-supervised learning, the investigated approach can successfully learn feature representation specific to a given dataset for retrieval. This representation can be made even more effective by boosting it with image similarity information mined from the dataset. As experimentally validated, such a simple ``self-supervised learning + self-boosting'' approach can well compete with the relevant state-of-the-art retrieval methods. Ablation study is conducted to show the appealing properties of this approach and its limitation on generalisation across datasets.

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