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Memento 2.0: an improved lifelog search engine for LSC’22

Alam, Naushad orcid logoORCID: 0000-0002-3144-5622, Graham, Yvette orcid logoORCID: 0000-0001-6741-4855 and Gurrin, Cathal orcid logoORCID: 0000-0003-2903-3968 (2022) Memento 2.0: an improved lifelog search engine for LSC’22. In: 5th Annual Lifelog Search Challenge (LSC ’22), ICMR 2022, 27-30 June 2022, Newark, NJ, USA. ISBN 978-1-4503-9239-6

In this paper, we present Memento 2.0, an improved version of our system which first participated in the Lifelog Search Challenge 2021. Memento 2.0 employs image-text embeddings derived from two CLIP models (ViT-L/14 and ResNet-50x64) and adopts a weighted ensemble approach to derive a combined final ranking. Our approach significantly improves the performance over the baseline LSC’21 system. We additionally make important updates to the system’s user interface after analysing the shortcomings to make it more efficient and better suited to the needs of the Lifelog Search Challenge.
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Uncontrolled Keywords:lifelog; semantic image representation
Subjects:Computer Science > Information retrieval
Computer Science > Multimedia systems
Computer Science > Information storage and retrieval systems
Computer Science > Lifelog
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > INSIGHT Centre for Data Analytics
Published in: Proceedings of the 5th Annual Lifelog Search Challenge (LSC’22). . Association for Computer Machinery (ACM). ISBN 978-1-4503-9239-6
Publisher:Association for Computer Machinery (ACM)
Official URL:https://doi.org/10.1145/3512729.3533006
Copyright Information:© 2022 The Authors.
Funders:Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289_P2, co-funded by the European Regional Development Fund
ID Code:28998
Deposited On:13 Sep 2023 10:24 by Naushad Alam . Last Modified 13 Sep 2023 10:24

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