Combination of Multiple Global Descriptors for Image Retrieval
HeeJae Jun, Byungsoo Ko, Youngjoon Kim, Insik Kim, Jongtack Kim

TL;DR
This paper introduces a flexible, end-to-end framework that combines multiple global descriptors for image retrieval, achieving state-of-the-art results without training separate models.
Contribution
It presents a novel, end-to-end trainable framework for combining global descriptors, improving image retrieval performance efficiently.
Findings
Outperforms single global descriptors in retrieval tasks
Achieves state-of-the-art results on multiple benchmarks
Flexible and expandable framework
Abstract
Recent studies in image retrieval task have shown that ensembling different models and combining multiple global descriptors lead to performance improvement. However, training different models for the ensemble is not only difficult but also inefficient with respect to time and memory. In this paper, we propose a novel framework that exploits multiple global descriptors to get an ensemble effect while it can be trained in an end-to-end manner. The proposed framework is flexible and expandable by the global descriptor, CNN backbone, loss, and dataset. Moreover, we investigate the effectiveness of combining multiple global descriptors with quantitative and qualitative analysis. Our extensive experiments show that the combined descriptor outperforms a single global descriptor, as it can utilize different types of feature properties. In the benchmark evaluation, the proposed framework…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Image Retrieval and Classification Techniques · Domain Adaptation and Few-Shot Learning
