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Evaluating contrastive models for instance-based image retrieval

Krishna, Tarun, McGuinness, Kevin orcid logoORCID: 0000-0003-1336-6477 and O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 (2021) Evaluating contrastive models for instance-based image retrieval. In: ACM International Conference on Multimedia Retrieval, 21-24 Aug 2021, Taipei, Taiwan. ISBN 978-1-4503-8463-6

Abstract
In this work, we evaluate contrastive models for the task of imageretrieval. We hypothesise that models that are learned to encodesemantic similarity among instances via discriminative learningshould perform well on the task of image retrieval, where rele-vancy is defined in terms of instances of the same object. Throughour extensive evaluation, we find that representations from mod-els trained using contrastive methods perform on-par with (andoutperforms) a pre-trained supervised baseline trained on the Ima-geNet labels in retrieval tasks under various configurations. This isremarkable given that the contrastive models require no explicitsupervision. Thus, we conclude that these models can be used tobootstrap base models to build more robust image retrieval engines.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Contrastive Learning; Instance Retrieval; Deep Learning
Subjects:UNSPECIFIED
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Institutes and Centres > INSIGHT Centre for Data Analytics
Published in: ICMR '21: Proceedings of the 2021 International Conference on Multimedia Retrieval. . Association for Computing Machinery (ACM). ISBN 978-1-4503-8463-6
Publisher:Association for Computing Machinery (ACM)
Official URL:https://doi.org/10.1145/3460426.3463585
Copyright Information:© 2021 The Authors (CC-BY-4.0)
ID Code:25806
Deposited On:07 Sep 2021 13:53 by Tarun Krishna . Last Modified 09 Nov 2021 16:10
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